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100644 docs/search.json create mode 100644 docs/sitemap.xml diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml index 6cce515c..46f531d1 100644 --- a/.github/workflows/pkgdown.yaml +++ b/.github/workflows/pkgdown.yaml @@ -6,7 +6,7 @@ on: - main - master tags: - -'*' + - '*' jobs: build: @@ -47,12 +47,16 @@ jobs: - name: Setup renv uses: r-lib/actions/setup-renv@v2 - # Install the package + # Install Pandoc + - name: Setup Pandoc + uses: r-lib/actions/setup-pandoc@v2 + + # Install the package and its dependencies - name: Install devtools and the RoBMA Package run: | install.packages('devtools') install.packages('pkgdown') - install.packages(c('metaBMA', 'metafor', 'weightr', 'lme4', 'fixest', 'emmeans', 'metadat', 'vdiffr', 'testthat', 'covr', 'pandoc')) + install.packages(c('metaBMA', 'metafor', 'weightr', 'lme4', 'fixest', 'emmeans', 'metadat', 'vdiffr', 'testthat', 'covr')) devtools::install() shell: Rscript {0} diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 00000000..15655d6d --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,3 @@ +url: 'https://https://fbartos.github.io/RoBMA/' +template: + bootstrap: 5 \ No newline at end of file diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 00000000..ff9091b1 --- /dev/null +++ b/docs/404.html @@ -0,0 +1,87 @@ + + + + + + + +Page not found (404) • RoBMA + + + + + + + + Skip to contents + + +
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+ +Content not found. Please use links in the navbar. + +
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+

Developed by František Bartoš, Maximilian Maier.

+
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+

Site built with pkgdown 2.1.1.

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+ + + + + + + diff --git a/docs/articles/CustomEnsembles.html b/docs/articles/CustomEnsembles.html new file mode 100644 index 00000000..987dd2d3 --- /dev/null +++ b/docs/articles/CustomEnsembles.html @@ -0,0 +1,519 @@ + + + + + + + +Fitting Custom Meta-Analytic Ensembles • RoBMA + + + + + + + + Skip to contents + + +
+ + + + +
+
+ +

Fitting Custom Meta-Analytic Ensembles

+

František +Bartoš

+ +

2024-12-11

+ + Source: vignettes/CustomEnsembles.Rmd +
CustomEnsembles.Rmd
+
+ + + +

This vignette provides a step-by-step guide to fitting custom +meta-analytic ensembles using the RoBMA R package. By the end of this +guide, you will be able to construct and evaluate custom meta-analytic +models.

+

By default, the RoBMA() function specifies models as a +combination of all supplied prior distributions (across null and +alternative specification), with their prior model weights being equal +to the product of prior distributions’ weights. This results in the 36 +meta-analytic models using the default settings (Bartoš et al., +2023)1^1. +In another vignette, we illustrated +that RoBMA can be also utilized for reproducing Bayesian Model-Averaged +Meta-Analysis (BMA) (Bartoš et al., 2021; Gronau +et al., 2017, 2021). However, the package was built as a +framework for estimating highly customized meta-analytic model +ensembles. Here, we are going to illustrate how to do exactly that (see +Bartoš et al. (2022) for a tutorial paper +on customizing the model ensemble with JASP).

+

Please keep in mind that all models should be justified by theory. +Furthermore, the models should be tested to make sure that the ensemble +can perform as intended a priori to drawing inference from it. The +following sections are only for illustrating the functionality of the +package. We provide a complete discussion with the relevant sources in +the Example section of Bartoš et al. +(2023).

+
+

The Dataset +

+

To illustrate the custom model building procedure, we use data from +the infamous Bem (2011) “Feeling the +future” precognition study. We use coding of the results as summarized +by Bem in one of his later replies (Bem et al., +2011).

+
+library(RoBMA)
+#> Loading required namespace: runjags
+#> Loading required namespace: mvtnorm
+
+data("Bem2011", package = "RoBMA")
+Bem2011
+#>      d         se                                        study
+#> 1 0.25 0.10155048                  Detection of Erotic Stimuli
+#> 2 0.20 0.08246211                Avoidance of Negative Stimuli
+#> 3 0.26 0.10323629                        Retroactive Priming I
+#> 4 0.23 0.10182427                       Retroactive Priming II
+#> 5 0.22 0.10120277  Retroactive Habituation I - Negative trials
+#> 6 0.15 0.08210765 Retroactive Habituation II - Negative trials
+#> 7 0.09 0.07085372             Retroactive Induction of Boredom
+#> 8 0.19 0.10089846                     Facilitation of Recall I
+#> 9 0.42 0.14752627                    Facilitation of Recall II
+
+
+

The Custom Ensemble +

+

We consider the following scenarios as plausible explanations for the +data, and decide to include only those models into the meta-analytic +ensemble:

+
    +
  1. there is absolutely no precognition effect - a fixed effects model +assuming the effect size to be zero +(H0fH_{0}^f),
    2. +
  2. the experiments measured the same underlying precognition effect - a +fixed effects model +(H1fH_{1}^f),
    3. +
  3. each of the experiments measured a slightly different precognition +effect - a random effects model +(H1rH_{1}^r),
    4. +
  4. there is absolutely no precognition effect and the results can be +explained by publication bias, modeled with one of the following +publication bias adjustments: - 4.1) one-sided selection operating on +significant p-values +(H1,pb1fH_{1,\text{pb1}}^f), +- 4.2) one-sided selection operating on significant and marginally +significant p-values +(H1,pb2fH_{1,\text{pb2}}^f), +- 4.3) PET correction for publication bias which adjusts for the +relationship between effect sizes and standard errors +(H1,pb3fH_{1,\text{pb3}}^f), +- 4.4) PEESE correction for publication bias which adjusts for the +relationship between effect sizes and standard errors squared +(H1,pb4fH_{1,\text{pb4}}^f).
    5. +
+

If we were to fit the ensemble using the RoBMA() +function and specifying all of the priors, we would have ended with 2 +(effect or no effect) * 2 (heterogeneity or no heterogeneity) * 5 (no +publication bias or 4 ways of adjusting for publication bias) = 20 +models. That is 13 models more than requested. Furthermore, we could not +specify different parameters for the prior distributions for each model. +The following process allows this, though we do not utilize it here.

+

We start with fitting only the first model using the +RoBMA() function and we will continuously update the fitted +object to include all of the models.

+
+

Model 1 +

+

We initiate the model ensemble by specifying only the first model +with the RoBMA() function. We explicitly specify prior +distributions for all components and set the prior distributions to +correspond to the null hypotheses and set the seed to ensure +reproducibility of the results.

+
+fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study,
+             priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL,
+             priors_effect_null        = prior("spike", parameters = list(location = 0)),
+             priors_heterogeneity_null = prior("spike", parameters = list(location = 0)),
+             priors_bias_null          = prior_none(),
+             seed = 1)
+

We verify that the ensemble contains only the single specified model +with the summary() function by setting +type = "models".

+
+summary(fit, type = "models")
+#> Call:
+#> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, 
+#>     priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, 
+#>     priors_effect_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_heterogeneity_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_bias_null = prior_none(), seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Models overview:
+#>  Model Prior Effect Prior Heterogeneity Prior prob. log(marglik) Post. prob.
+#>      1     Spike(0)            Spike(0)       1.000        -3.28       1.000
+#>  Inclusion BF
+#>           Inf
+
+
+

Model 2 +

+

Before we add the second model to the ensemble, we need to decide on +the prior distribution for the mean parameter. If precognition were to +exist, the effect would be small since all casinos would be bankrupted +otherwise. The effect would also be positive, since any deviation from +randomness could be characterized as an effect. Therefore, we decide to +use a normal distribution with mean = 0.15, standard deviation 0.10, and +truncated to the positive range. This sets the prior density around +small effect sizes. To get a better grasp of the prior distribution, we +visualize it using the plot()) function (the figure can +also be created using the ggplot2 package by adding +plot_type = "ggplot" argument).

+
+plot(prior("normal", parameters = list(mean = .15, sd = .10), truncation = list(lower = 0)))
+

+

We add the second model to the ensemble using the +update.RoBMA() function. The function can also be used for +many other purposes - updating settings, prior model weights, and +refitting failed models. Here, we supply the fitted ensemble object and +add an argument specifying the prior distributions of each component for +the additional model. Since we want to add Model 2 - we set the prior +for the +μ\mu +parameter to be treated as a prior belonging to the alternative +hypothesis of the effect size component and the remaining priors treated +as belonging to the null hypotheses. If we wanted, we could also specify +prior_weights argument, to change the prior probability of +the fitted model but we do not utilize this option here and keep the +default value, which sets the prior weights for the new model to +1. (Note that the arguments for specifying prior +distributions in update.RoBMA() function are +prior_X - in singular, in comparison to +RoBMA() function that uses priors_X in +plural.)

+
+fit <- update(fit,
+              prior_effect             = prior("normal", parameters = list(mean = .15, sd = .10), truncation = list(lower = 0)),
+              prior_heterogeneity_null = prior("spike",  parameters = list(location = 0)),
+              prior_bias_null          = prior_none())
+

We can again inspect the updated ensemble to verify that it contains +both models. We see that Model 2 notably outperformed the first model +and attained all of the posterior model probability.

+
+summary(fit, type = "models")
+#> Call:
+#> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, 
+#>     priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, 
+#>     priors_effect_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_heterogeneity_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_bias_null = prior_none(), seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Models overview:
+#>  Model        Prior Effect       Prior Heterogeneity Prior prob. log(marglik)
+#>      1                  Spike(0)            Spike(0)       0.500        -3.28
+#>      2 Normal(0.15, 0.1)[0, Inf]            Spike(0)       0.500        14.91
+#>  Post. prob. Inclusion BF
+#>        0.000        0.000
+#>        1.000 79422247.251
+
+
+

Models 3-4.4 +

+

Before we add the remaining models to the ensemble using the +update() function, we need to decide on the remaining prior +distributions. Specifically, on the prior distribution for the +heterogeneity parameter +τ\tau, +and the publication bias adjustment parameters +ω\omega +(for the selection models’ weightfunctions) and PET and PEESE for the +PET and PEESE adjustment.

+

For Model 3, we use the usual inverse-gamma(1, .15) prior +distribution based on empirical heterogeneity estimates (Erp et al., 2017) for the heterogeneity +parameter +τ\tau. +For Models 4.1-4.4 we use the default settings for the publication bias +adjustments as outlined in the Appendix B of (Bartoš et al., 2023).

+

Now, we just need to add the remaining models to the ensemble using +the update() function as already illustrated.

+
+### adding Model 3
+fit <- update(fit,
+              prior_effect        = prior("normal", parameters = list(mean = .15, sd = .10), truncation = list(lower = 0)),
+              prior_heterogeneity = prior("invgamma", parameters = list(shape = 1, scale = .15)),
+              prior_bias_null     = prior_none())
+
+### adding Model 4.1
+fit <- update(fit,
+              prior_effect_null        = prior("spike",     parameters = list(location = 0)),
+              prior_heterogeneity_null = prior("spike",     parameters = list(location = 0)),
+              prior_bias               = prior_weightfunction("one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05))))
+              
+### adding Model 4.2
+fit <- update(fit,
+              prior_effect_null        = prior("spike",     parameters = list(location = 0)),
+              prior_heterogeneity_null = prior("spike",     parameters = list(location = 0)),
+              prior_bias               = prior_weightfunction("one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10))))
+              
+### adding Model 4.3
+fit <- update(fit,
+              prior_effect_null        = prior("spike",     parameters = list(location = 0)),
+              prior_heterogeneity_null = prior("spike",     parameters = list(location = 0)),
+              prior_bias               = prior_PET("Cauchy", parameters = list(0, 1),  truncation = list(lower = 0)))
+              
+### adding Model 4.4
+fit <- update(fit,
+              prior_effect_null        = prior("spike",     parameters = list(location = 0)),
+              prior_heterogeneity_null = prior("spike",     parameters = list(location = 0)),
+              prior_bias               = prior_PEESE("Cauchy", parameters = list(0, 5),  truncation = list(lower = 0)))
+

We again verify that all of the requested models are included in the +ensemble using the summary()) function with +type = "models" argument.

+
+summary(fit, type = "models")
+#> Call:
+#> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, 
+#>     priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, 
+#>     priors_effect_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_heterogeneity_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_bias_null = prior_none(), seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Models overview:
+#>  Model        Prior Effect       Prior Heterogeneity
+#>      1                  Spike(0)            Spike(0)
+#>      2 Normal(0.15, 0.1)[0, Inf]            Spike(0)
+#>      3 Normal(0.15, 0.1)[0, Inf]   InvGamma(1, 0.15)
+#>      4                  Spike(0)            Spike(0)
+#>      5                  Spike(0)            Spike(0)
+#>      6                  Spike(0)            Spike(0)
+#>      7                  Spike(0)            Spike(0)
+#>                      Prior Bias                     Prior prob. log(marglik)
+#>                                                           0.143        -3.28
+#>                                                           0.143        14.91
+#>                                                           0.143        12.85
+#>       omega[one-sided: .05] ~ CumDirichlet(1, 1)          0.143        13.70
+#>   omega[one-sided: .1, .05] ~ CumDirichlet(1, 1, 1)       0.143        12.58
+#>                         PET ~ Cauchy(0, 1)[0, Inf]        0.143        15.75
+#>                       PEESE ~ Cauchy(0, 5)[0, Inf]        0.143        15.65
+#>  Post. prob. Inclusion BF
+#>        0.000        0.000
+#>        0.168        1.210
+#>        0.021        0.132
+#>        0.050        0.318
+#>        0.016        0.100
+#>        0.391        3.845
+#>        0.353        3.278
+
+
+
+

Using the Fitted Ensemble +

+

Finally, we use the summary() function to inspect the +model results. The results from our custom ensemble indicate weak +evidence for the absence of the precognition effect, +BF10=0.584\text{BF}_{10} = 0.584 +-> +BF01=1.71\text{BF}_{01} = 1.71, +moderate evidence for the absence of heterogeneity, +BFrf=0.132\text{BF}_{\text{rf}} = 0.132 +-> +BFfr=7.58\text{BF}_{\text{fr}} = 7.58, +and moderate evidence for the presence of the publication bias, +BFpb=3.21\text{BF}_{\text{pb}} = 3.21.

+
+summary(fit)
+#> Call:
+#> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, 
+#>     priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, 
+#>     priors_effect_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_heterogeneity_null = prior("spike", parameters = list(location = 0)), 
+#>     priors_bias_null = prior_none(), seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/7       0.286       0.189        0.584
+#> Heterogeneity    1/7       0.143       0.021        0.132
+#> Bias             4/7       0.571       0.811        3.212
+#> 
+#> Model-averaged estimates:
+#>                  Mean Median 0.025  0.975
+#> mu              0.036  0.000 0.000  0.226
+#> tau             0.002  0.000 0.000  0.000
+#> omega[0,0.05]   1.000  1.000 1.000  1.000
+#> omega[0.05,0.1] 0.938  1.000 0.014  1.000
+#> omega[0.1,1]    0.935  1.000 0.012  1.000
+#> PET             0.820  0.000 0.000  2.601
+#> PEESE           7.284  0.000 0.000 25.508
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

The finalized ensemble can be treated as any other RoBMA +ensemble using the summary(), plot(), +plot_models(), forest(), and +diagnostics() functions. For example, we can use the +plot.RoBMA() with the +parameter = "mu", prior = TRUE arguments to plot the prior +(grey) and posterior distribution (black) for the effect size. The +function visualizes the model-averaged estimates across all models by +default. The arrows represent the probability mass at the value 0 (a +spike at 0). The secondary y-axis (right) shows the probability mass at +the zero effect size, which increased from the prior probability of 0.71 +to the posterior the posterior probability of 0.81.

+
+plot(fit, parameter = "mu", prior = TRUE)
+

+

We can also inspect the posterior distributions of the publication +bias adjustments. To visualize the model-averaged weightfunction, we set +parameter = weightfunction argument. The resulting figure +shows the light gray prior distribution and the dark gray the posterior +distribution.

+
+plot(fit, parameter = "weightfunction", prior = TRUE)
+

+

We can also inspect the posterior estimate of the regression +relationship between the standard errors and effect sizes by setting +parameter = "PET-PEESE".

+
+plot(fit, parameter = "PET-PEESE", prior = TRUE)
+

+
+
+

Footnotes +

+

1^1 +- The default setting used to produce 12 models in RoBMA versions < +2, which corresponded to an earlier an article by Maier et al. (2023) in which we applied Bayesian +model-averaging only across selection models.

+
+
+

References +

+
+
+Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., & +Wagenmakers, E.-J. (2021). Bayesian model-averaged meta-analysis in +medicine. Statistics in Medicine, 40(30), 6743–6761. +https://doi.org/10.1002/sim.9170 +
+
+Bartoš, F., Maier, Maximilian, Quintana, D. S., & Wagenmakers, E.-J. +(2022). Adjusting for publication bias in JASP and +R — Selection models, PET-PEESE, +and robust Bayesian meta-analysis. Advances in Methods +and Practices in Psychological Science, 5(3), 1–19. https://doi.org/10.1177/25152459221109259 +
+
+Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & +Stanley, T. D. (2023). Robust Bayesian meta-analysis: +Model-averaging across complementary publication bias +adjustment methods. Research Synthesis Methods, 14(1), +99–116. https://doi.org/10.1002/jrsm.1594 +
+
+Bem, D. J. (2011). Feeling the future: Experimental +evidence for anomalous retroactive influences on cognition and affect. +Journal of Personality and Social Psychology, 100(3), +407–425. https://doi.org/10.1037/a0021524 +
+
+Bem, D. J., Utts, J., & Johnson, W. O. (2011). Must psychologists +change the way they analyze their data? Journal of Personality and +Social Psychology, 101(4), 716–719. https://doi.org/10.1037/a0024777 +
+
+Erp, S. van, Verhagen, J., Grasman, R. P., & Wagenmakers, E.-J. +(2017). Estimates of between-study heterogeneity for 705 meta-analyses +reported in Psychological Bulletin from +1990–2013. Journal of Open Psychology Data, 5(1), 1–5. +https://doi.org/10.5334/jopd.33 +
+
+Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., & +Wagenmakers, E.-J. (2021). A primer on Bayesian +model-averaged meta-analysis. Advances in Methods and Practices in +Psychological Science, 4(3), 1–19. https://doi.org/10.1177/25152459211031256 +
+
+Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., +& Wagenmakers, E.-J. (2017). A Bayesian model-averaged +meta-analysis of the power pose effect with informed and default priors: +The case of felt power. Comprehensive Results in Social +Psychology, 2(1), 123–138. https://doi.org/10.1080/23743603.2017.1326760 +
+
+Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2023). Robust +Bayesian meta-analysis: Addressing publication +bias with model-averaging. Psychological Methods, +28(1), 107–122. https://doi.org/10.1037/met0000405 +
+
+
+
+
+ + + +
+

Developed by František Bartoš, Maximilian Maier.

+
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+

Site built with pkgdown 2.1.1.

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+ + + + +
+
+ +

Hierarchical Bayesian Model-Averaged Meta-Analysis

+

František +Bartoš

+ +

2024-12-11

+ + Source: vignettes/HierarchicalBMA.Rmd +
HierarchicalBMA.Rmd
+
+ + + +

Hierarchical (or multilevel/3-level) meta-analysis adjusts for the +dependency of effect sizes due to clustering in the data. For example, +effect size estimates from multiple experiments reported in the same +manuscript might be expected to be more similar than effect sizes from a +different paper (Konstantopoulos, 2011). +This vignette illustrates how to deal with such dependencies among +effect size estimates (in cases with simple nested structure) using the +Bayesian model-averaged meta-analysis (BMA) (Bartoš et al., 2021; Gronau et al., 2017, +2021). (See other vignettes for more details on BMA: Reproducing BMA or Informed BMA in medicine.)

+

First, we introduce the example data set. Second, we illustrate the +frequentist hierarchical meta-analysis with the metafor R +package and discuss the results. Third, we outline the hierarchical +meta-analysis parameterization. Fourth, we estimate the Bayesian +model-averaged hierarchical meta-analysis. Finally, we conclude by +discussing further extensions and publication bias adjustment.

+
+

Example Data Set +

+

We use the dat.konstantopoulos2011 data set from the +metadat R package (Thomas et al., +2019) that is used for the same functionality in the metafor +(Wolfgang, 2010) R package. We roughly +follow the example in the data set’s help file, +?dat.konstantopoulos2011. The data set consists of 56 +studies estimating the effects of modified school calendars on students’ +achievement. The 56 studies were run in individual schools, which can be +grouped into 11 districts. We might expect more similar effect size +estimates from schools in the same district – in other words, the effect +size estimates from the same district might not be completely +independent. Consequently, we might want to adjust for this dependency +(clustering) between the effect size estimates to draw a more +appropriate inference.

+

First, we load the data set, assign it to the dat +object, and inspect the first few rows.

+
+data("dat.konstantopoulos2011", package = "metadat")
+dat <- dat.konstantopoulos2011
+
+head(dat)
+#>   district school study year    yi    vi
+#> 1       11      1     1 1976 -0.18 0.118
+#> 2       11      2     2 1976 -0.22 0.118
+#> 3       11      3     3 1976  0.23 0.144
+#> 4       11      4     4 1976 -0.30 0.144
+#> 5       12      1     5 1989  0.13 0.014
+#> 6       12      2     6 1989 -0.26 0.014
+

In the following analyses, we use the following variables:

+
    +
  • +yi, standardized mean differences,
    • +
  • +vi, sampling variances of the standardized mean +differences,
    • +
  • +district, district id which distinguishes among the +districts,
    • +
  • and school, that distinguishes among different schools +within the same district.
    • +
+
+
+

Frequentist Hierarchical Meta-Analysis with +metafor +

+

We follow the data set’s help file and fit a simple random effects +meta-analysis using the rma() function from +metafor package. This model ignores the dependency between +effect size estimates. We use this simple model as our starting point +and as a comparison with the later models.

+
+fit_metafor.0 <- metafor::rma(yi = yi, vi = vi, data = dat)
+fit_metafor.0
+#> 
+#> Random-Effects Model (k = 56; tau^2 estimator: REML)
+#> 
+#> tau^2 (estimated amount of total heterogeneity): 0.0884 (SE = 0.0202)
+#> tau (square root of estimated tau^2 value):      0.2974
+#> I^2 (total heterogeneity / total variability):   94.70%
+#> H^2 (total variability / sampling variability):  18.89
+#> 
+#> Test for Heterogeneity:
+#> Q(df = 55) = 578.8640, p-val < .0001
+#> 
+#> Model Results:
+#> 
+#> estimate      se    zval    pval   ci.lb   ci.ub     
+#>   0.1279  0.0439  2.9161  0.0035  0.0419  0.2139  ** 
+#> 
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

The model summary returns a small but statistically significant +effect size estimate +μ=0.128\mu = 0.128 +(se=0.044\text{se} = 0.044) +and a considerable heterogeneity estimate +τ=0.297\tau = 0.297.

+

We extend the model to account for the hierarchical structure of the +data, i.e., schools within districts, by using the rma.mv() +function from the metafor package and extending it with the +random = ~ school | district argument.

+
+fit_metafor <- metafor::rma.mv(yi, vi, random = ~ school | district, data = dat)
+fit_metafor
+#> 
+#> Multivariate Meta-Analysis Model (k = 56; method: REML)
+#> 
+#> Variance Components:
+#> 
+#> outer factor: district (nlvls = 11)
+#> inner factor: school   (nlvls = 11)
+#> 
+#>             estim    sqrt  fixed 
+#> tau^2      0.0978  0.3127     no 
+#> rho        0.6653             no 
+#> 
+#> Test for Heterogeneity:
+#> Q(df = 55) = 578.8640, p-val < .0001
+#> 
+#> Model Results:
+#> 
+#> estimate      se    zval    pval   ci.lb   ci.ub    
+#>   0.1847  0.0846  2.1845  0.0289  0.0190  0.3504  * 
+#> 
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

We find that accounting for the hierarchical structure of the data +results in (1) a slightly larger effect size estimate +(μ=0.187\mu = 0.187) +and (2) larger standard error of the effect size estimate +(se=0.085\text{se} = 0.085). +The larger standard error is a natural consequence of accounting for the +dependency between the effect sizes. Because the effect sizes are +dependent, they contribute less additional information than independent +effect sizes would. Specifying the hierarchical model then accounts for +the dependency by estimating similarity between the estimates from the +same cluster (school) and discounting the information borrowed from each +estimate. The estimate of the similarity among estimates from the same +cluster is summarized in the \rho = 0.666 estimate.

+
+
+

Specifications of Hierarchical Meta-Analysis +

+

We specify a simple hierarchical meta-analytic model (see Konstantopoulos (2011) for an example). Using +distributional notation, we can describe the data generating process as +a multi-stage sampling procedure. In a nutshell, we assume the existence +of an overall mean effect +μ\mu. +Next, we assume that the effect sizes in each district +k=1,…,Kk = 1, \dots, K, +γk\gamma_k, +systematically differ from the mean effect, with the variance of the +district-level effects summarized with heterogeneity +τb\tau_{b} +(as between). Furthermore, we assume that the true effects +θk,j\theta_{k,j} +of each study +j=1,…Jkj = 1, \dots J_k +systematically differ from the district-level effect, with the variance +of the study effects from the district-level effect summarized with +heterogeneity +τw\tau_{w} +(as within). Finally, the observed effect sizes +yk,jy_{k,j} +that differ from the true effects +yk,jy_{k,j} +due to random errors +sek,j\text{se}_{k,j}.

+

Mathematically, we can describe such a model as: +γk∼N(μ,τb2),θk,j∼N(γk,τw2),yk,j∼N(θk,j,sek,j). +\begin{aligned} + \gamma_k &\sim \text{N}(\mu, \tau_b^2),\\ + \theta_{k,j} &\sim \text{N}(\gamma_k, \tau_w^2),\\ + y_{k,j} &\sim \text{N}(\theta_{k,j}, \text{se}_{k,j}).\\ +\end{aligned} + Where N() denotes a normal distribution +with mean and variance.

+

Conveniently, and with a bit of algebra, we do not need to estimate +the district-level and true study effects. Instead, we marginalize them +out, and we sample the observed effect sizes from each district +yk,.y_{k,.} +directly from a multivariate normal distributions, MN(), with a common +mean +μ\mu +and covariance matrix S: +yk,.∼MN(μ,S),S=[τb2+τw2+se12τw2…τw2τw2τb2+τw2+se22…τw2…………τw2τw2…τb2+τw2+seJk2]. +\begin{aligned} + y_{k,.} &\sim \text{MN}(\mu, \text{S}),\\ + \text{S} &= \begin{bmatrix} + \tau_b^2 + \tau_w^2 + \text{se}_1^2 & \tau_w^2 & \dots & \tau_w^2 \\ + \tau_w^2 & \tau_b^2 + \tau_w^2 + \text{se}_2^2 & \dots & \tau_w^2 \\ + \dots & \dots & \dots & \dots \\ + \tau_w^2 & \tau_w^2 & \dots & \tau_b^2 + \tau_w^2 + \text{se}_{J_k}^2 & \\ + \end{bmatrix}. +\end{aligned} + The random effects marginalization is +helpful as it allows us to sample fewer parameters from the posterior +distribution (which significantly simplifies marginal likelihood +estimation via bridge sampling). Furthermore, the marginalization allows +us to properly specify selection model publication bias adjustment +models – the marginalization propagates the selection process up through +all the sampling steps at once (we cannot proceed with the sequential +sampling as the selection procedure on the observed effect sizes +modifies the sampling distributions of all the preceding levels).

+

We can further re-parameterize the model by performing the following +substitution, +τ2=τb2+τw2,ρ=τw2τb2+τw2, +\begin{aligned} + \tau^2 &= \tau_b^2 + \tau_w^2,\\ + \rho &= \frac{\tau_w^2}{\tau_b^2 + \tau_w^2}, +\end{aligned} + and specifying the covariance matrix +using the inter-study correlation +ρ\rho, +total heterogeneity +τ\tau, +and the standard errors +se.\text{se}_{.}: +S=[τ2+se12ρτ2…ρτ2ρτ2τ2+se22…ρτ2…………ρτ2ρτ2…τ2+seJk2]. +\begin{aligned} + \text{S} &= \begin{bmatrix} + \tau^2 + \text{se}_1^2 & \rho\tau^2 & \dots & \rho\tau^2 \\ + \rho\tau^2 & \tau^2 + \text{se}_2^2 & \dots & \rho\tau^2 \\ + \dots & \dots & \dots & \dots \\ + \rho\tau^2 & \rho\tau^2 & \dots & \tau^2 + \text{se}_{J_k}^2 & \\ + \end{bmatrix}. +\end{aligned} + This specification corresponds to the +compound symmetry covariance matrix of random effects, the default +settings in the metafor::rma.mv() function. More +importantly, it allows us to easily specify prior distributions on the +correlation coefficient +ρ\rho +and the total heterogeneity +τ\tau.

+
+
+

Hierarchical Bayesian Model-Averaged Meta-Analysis with +RoBMA +

+

Before we estimate the complete Hierarchical Bayesian Model-Averaged +Meta-Analysis (hBMA) with the RoBMA package, we briefly +reproduce the simpler models we estimated with the metafor +package in the previous section.

+
+

Bayesian Random Effects Meta-Analysis +

+

First, we estimate a simple Bayesian random effects meta-analysis +(corresponding to fit_metafor.0). We use +the RoBMA() function and specify the effect sizes and +sampling variances via the d = dat$yi and +v = dat$vi arguments. We set the +priors_effect_null, priors_heterogeneity_null, +and priors_bias arguments to null to omit models assuming +the absence of the effect, heterogeneity, and the publication bias +adjustment components.

+
+fit.0 <- RoBMA(d = dat$yi, v = dat$vi,
+               priors_effect_null        = NULL,
+               priors_heterogeneity_null = NULL,
+               priors_bias               = NULL,
+               parallel = TRUE, seed = 1)
+

We generate a complete summary for the only estimated model by adding +the type = "individual" argument to the +summary() function.

+
+summary(fit.0, type = "individual")
+#> Call:
+#> RoBMA(d = dat$yi, v = dat$vi, priors_bias = NULL, priors_effect_null = NULL, 
+#>     priors_heterogeneity_null = NULL, parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis                                                               
+#>  Model              1             Parameter prior distributions
+#>  Prior prob.    1.000                    mu ~ Normal(0, 1)     
+#>  log(marglik)   17.67                   tau ~ InvGamma(1, 0.15)
+#>  Post. prob.    1.000                                          
+#>  Inclusion BF     Inf                                          
+#> 
+#> Parameter estimates:
+#>      Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat
+#> mu  0.126 0.043 0.041  0.127 0.211     0.00044          0.010 9757 1.000
+#> tau 0.292 0.033 0.233  0.290 0.364     0.00034          0.010 9678 1.000
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

We verify that the effect size, +μ=0.126\mu = 0.126 +(95% CI [0.041,0.211]\text{95% CI } [0.041, 0.211]), +and heterogeneity, +τ=0.292\tau = 0.292 +(95% CI [0.233,0.364]\text{95% CI } [0.233, 0.364]), +estimates closely correspond to the frequentist results (as we would +expect from parameter estimates under weakly informative priors).

+
+
+

Hierarchical Bayesian Random Effects Meta-Analysis +

+

Second, we account for the clustered effect size estimates within +districts by extending the previous function call with the +study_ids = dat$district argument. This allows us to +estimate the hierarchical Bayesian random effects meta-analysis +(corresponding to fit_metafor). We use the default prior +distribution for the correlation parameter +\rho \sim \text{Beta}(1, 1), set via the +priors_hierarchical argument, which restricts the +correlation to be positive and uniformly distributed on the interval +(0,1)(0, 1).

+
+fit <- RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district,
+             priors_effect_null        = NULL,
+             priors_heterogeneity_null = NULL,
+             priors_bias               = NULL,
+             parallel = TRUE, seed = 1)
+

Again, we generate the complete summary for the only estimated +model,

+
+summary(fit, type = "individual")
+#> Call:
+#> RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_bias = NULL, 
+#>     priors_effect_null = NULL, priors_heterogeneity_null = NULL, 
+#>     parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis                                                               
+#>  Model              1             Parameter prior distributions
+#>  Prior prob.    1.000                    mu ~ Normal(0, 1)     
+#>  log(marglik)   25.70                   tau ~ InvGamma(1, 0.15)
+#>  Post. prob.    1.000                   rho ~ Beta(1, 1)       
+#>  Inclusion BF     Inf                                          
+#> 
+#> Parameter estimates:
+#>      Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat
+#> mu  0.181 0.083 0.017  0.180 0.346     0.00088          0.011 9041 1.000
+#> tau 0.308 0.056 0.223  0.299 0.442     0.00090          0.016 3859 1.000
+#> rho 0.627 0.142 0.320  0.641 0.864     0.00219          0.015 4202 1.000
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

and verify that our estimates, again, correspond to the frequentist +counterparts, with the estimated effect size, +μ=0.181\mu = 0.181 +(95% CI [0.017,0.346]\text{95% CI } [0.017, 0.346]), +heterogeneity, +τ=0.308\tau = 0.308 +(95% CI [0.223,0.442]\text{95% CI } [0.223, 0.442]), +and correlation, +ρ=0.627\rho = 0.627 +(95% CI [0.320,0.864]\text{95% CI } [0.320, 0.864]).

+

We can further visualize the prior and posterior distribution of the +ρ\rho +parameter using the plot() function.

+
+par(mar = c(2, 4, 0, 0))
+plot(fit, parameter = "rho", prior = TRUE)
+

+
+
+

Hierarchical Bayesian Model-Averaged Meta-Analysis +

+

Third, we extend the previous model into a model ensemble that also +includes models assuming the absence of the effect and/or heterogeneity +(we do not incorporate models assuming presence of publication bias due +to computational complexity explained in the summary). Including those +additional models allows us to evaluate evidence in favor of the effect +and heterogeneity. Furthermore, specifying all those additional models +allows us to incorporate the uncertainty about the specified models and +weight the posterior distribution according to how well the models +predicted the data. We estimate the remaining models by removing the +priors_effect_null and +priors_heterogeneity_null arguments from the previous +function calls, which include the previously omitted models of no effect +and/or no heterogeneity.

+
+fit_BMA <- RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district,
+                 priors_bias = NULL,
+                 parallel = TRUE, seed = 1)
+

Now we generate a summary for the complete model-averaged ensemble by +not specifying any additional arguments in the summary() +function.

+
+summary(fit_BMA)
+#> Call:
+#> RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_bias = NULL, 
+#>     parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/4       0.500       0.478 9.170000e-01
+#> Heterogeneity    2/4       0.500       1.000 9.326943e+92
+#> Hierarchical     2/4       0.500       1.000 9.326943e+92
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025 0.975
+#> mu  0.087  0.000 0.000 0.314
+#> tau 0.326  0.317 0.231 0.472
+#> rho 0.659  0.675 0.354 0.879
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

We find the ensemble contains four models, the combination of models +assuming the presence/absence of the effect/heterogeneity, each with +equal prior model probabilities. Importantly, the models assuming +heterogeneity are also specified with the hierarchical structure and +account for the clustering. A comparison of the specified models reveals +weak evidence against the effect, +BF10=0.917\text{BF}_{10} = 0.917, +and extreme evidence for the presence of heterogeneity, +BFrf=9.3×1092\text{BF}_{\text{rf}} = 9.3\times10^{92}. +Moreover, we find that the Hierarchical component summary +has the same values as the Heterogeneity component summary +because the default settings specify that all models assuming the +presence of heterogeneity also include the hierarchical structure.

+

We also obtain the model-averaged posterior estimates that combine +the posterior estimates from all models according to the posterior model +probabilities, the effect size, +μ=0.087\mu = 0.087 +(95% CI [0.000,0.314]\text{95% CI } [0.000, 0.314]), +heterogeneity, +τ=0.326\tau = 0.326 +(95% CI [0.231,0.472]\text{95% CI } [0.231, 0.472]), +and correlation, +ρ=0.659\rho = 0.659 +(95% CI [0.354,0.879]\text{95% CI } [0.354, 0.879]).

+
+
+

Testing the Presence of Clustering +

+

In the previous analyses, we assumed that the effect sizes are indeed +clustered within the districts, and we only adjusted for the clustering. +However, the effect sizes within the same cluster may not be more +similar than effect sizes from different clusters. Now, we specify a +model ensemble that allows us to test this assumption by specifying two +sets of random effect meta-analytic models. The first set of models +assumes that there is indeed clustering and that the correlation of +random effects is uniformly distributed on the +(0,1)(0, 1) +interval (as in the previous analyses). The second set of models assumes +that there is no clustering, i.e., the correlation of random effects +ρ=0\rho = 0, +which simplifies the structured covariance matrix to a diagonal matrix. +Again, we model average across models assuming the presence and absence +of the effect to account for the model uncertainty.

+

To specify this ‘special’ model ensemble with the +RoBMA() function, we need to modify the previous model call +in the following ways. We removed the fixed effect models by specifying +the priors_heterogeneity_null = NULL +argument.1^1 +Furthermore, we specify the prior distribution for models assuming the +absence of the hierarchical structure by adding the +priors_hierarchical_null = prior(distribution = "spike", parameters = list("location" = 0)) +argument.

+
+hierarchical_test <- RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district,
+                           priors_heterogeneity_null = NULL,
+                           priors_hierarchical_null = prior(distribution = "spike", parameters = list("location" = 0)),
+                           priors_bias = NULL,
+                           parallel = TRUE, seed = 1)
+
+summary(hierarchical_test)
+#> Call:
+#> RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_bias = NULL, 
+#>     priors_heterogeneity_null = NULL, priors_hierarchical_null = prior(distribution = "spike", 
+#>         parameters = list(location = 0)), parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/4       0.500       0.478        0.917
+#> Heterogeneity    4/4       1.000       1.000          Inf
+#> Hierarchical     2/4       0.500       1.000     4624.794
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025 0.975
+#> mu  0.087  0.000 0.000 0.314
+#> tau 0.326  0.317 0.231 0.472
+#> rho 0.659  0.675 0.354 0.879
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

We summarize the resulting model ensemble and find out that the +Hierarchical component is no longer equivalent to the +Heterogeneity component – the new model specification +allowed us to compare random effect models assuming the presence of the +hierarchical structure to random effect models assuming the absence of +the hierarchical structure. The resulting inclusion Bayes factor of the +hierarchical structure shows extreme evidence in favor of clustering of +the effect sizes, +BFρρ‾=4624\text{BF}_{\rho\bar{\rho}} = 4624, +i.e., there is extreme evidence that the intervention results in more +similar effects within the districts.

+
+
+
+

Summary +

+

We illustrated how to estimate a hierarchical Bayesian model-averaged +meta-analysis using the RoBMA package. The hBMA model +allows us to test for the presence vs absence of the effect and +heterogeneity while simultaneously adjusting for clustered effect size +estimates. While the current implementation allows us to draw a fully +Bayesian inference, incorporate prior information, and acknowledge model +uncertainty, it has a few limitations in contrast to the +metafor package. E.g., the RoBMA package only +allows a simple nested random effects (i.e., estimates within studies, +schools within districts etc). The simple nesting allows us to break the +full covariance matrix into per cluster block matrices which speeds up +the already demanding computation. Furthermore, the computational +complexity significantly increases when considering selection models as +we need to compute an exponentially increasing number of multivariate +normal probabilities with the increasing cluster size (existence of +clusters with more than four studies makes the current implementation +impractical due to the computational demands). However, these current +limitations are not the end of the road, as we are exploring other +approaches (e.g., only specifying PET-PEESE style publication bias +adjustment and other dependency adjustments) in a future vignette.

+
+
+

Footnotes +

+

1^1 +We could also model-average across the hierarchical structure assuming +fixed effect models, i.e., +τ∼f(.)\tau \sim f(.) +and +ρ=1\rho = 1. +However specifying such a model ensemble is a beyond the scope of this +vignette, see Custom ensembles +vignette for some hints.

+
+
+

References +

+
+
+Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., & +Wagenmakers, E.-J. (2021). Bayesian model-averaged meta-analysis in +medicine. Statistics in Medicine, 40(30), 6743–6761. +https://doi.org/10.1002/sim.9170 +
+
+Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., & +Wagenmakers, E.-J. (2021). A primer on Bayesian +model-averaged meta-analysis. Advances in Methods and Practices in +Psychological Science, 4(3), 1–19. https://doi.org/10.1177/25152459211031256 +
+
+Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., +& Wagenmakers, E.-J. (2017). A Bayesian model-averaged +meta-analysis of the power pose effect with informed and default priors: +The case of felt power. Comprehensive Results in Social +Psychology, 2(1), 123–138. https://doi.org/10.1080/23743603.2017.1326760 +
+
+Konstantopoulos, S. (2011). Fixed effects and variance components +estimation in three-level meta-analysis. Research Synthesis +Methods, 2(1), 61–76. https://doi.org/10.1002/jrsm.35⁠ +
+
+Thomas, W., Daniel, N., Alistair, S., W. Kyle, H., & Wolfgang, V. +(2019). metadat: +Meta-analysis datasets. https://cran.r-project.org/package=metadat +
+
+Wolfgang, V. (2010). Conducting meta-analyses in R with the +metafor package. Journal of Statistical +Software, 36(3), 1–48. https://www.jstatsoft.org/v36/i03/ +
+
+
+
+
+ + + +
+

Developed by František Bartoš, Maximilian Maier.

+
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+

Site built with pkgdown 2.1.1.

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+ + + + +
+
+ +

Informed Bayesian Model-Averaged Meta-Analysis in Medicine

+

František +Bartoš

+ +

2024-12-11

+ + Source: vignettes/MedicineBMA.Rmd +
MedicineBMA.Rmd
+
+ + + +

Bayesian model-averaged meta-analysis allows researchers to +seamlessly incorporate available prior information into the analysis +(Bartoš et al., 2021; Gronau et al., 2017, +2021). This vignette illustrates how to do this with an example +from Bartoš et al. (2021), who developed +informed prior distributions for meta-analyses of continuous outcomes +based on the Cochrane database of systematic reviews. Then, we extend +the example by incorporating publication bias adjustment with robust +Bayesian meta-analysis (Bartoš et al., 2023; +Maier et al., 2023).

+
+

Reproducing Informed Bayesian Model-Averaged Meta-Analysis +(BMA) +

+

We illustrate how to fit the informed BMA (not adjusting for +publication bias) using the RoBMA R package. For this +purpose, we reproduce the dentine hypersensitivity example from Bartoš et al. (2021), who reanalyzed five +studies with a tactile outcome assessment that were subjected to a +meta-analysis by Poulsen et al. +(2006).

+

We load the dentine hypersensitivity data included in the +package.

+
+library(RoBMA)
+
+data("Poulsen2006", package = "RoBMA")
+Poulsen2006
+#>           d        se               study
+#> 1 0.9073050 0.2720456     STD-Schiff-1994
+#> 2 0.7207589 0.1977769  STD-Silverman-1996
+#> 3 1.3305829 0.2721648   STD-Sowinski-2000
+#> 4 1.7688872 0.2656483 STD-Schiff-2000-(2)
+#> 5 1.3286828 0.3582617     STD-Schiff-1998
+

To reproduce the analysis from the example, we need to set informed +empirical prior distributions for the effect sizes +(μ\mu) +and heterogeneity +(τ\tau) +parameters that Bartoš et al. (2021) +obtained from the Cochrane database of systematic reviews. We can either +set them manually,

+
+fit_BMA <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study,
+                 priors_effect        = prior(distribution = "t", parameters = list(location = 0, scale = 0.51, df = 5)),
+                 priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1.79, scale = 0.28)),
+                 priors_bias          = NULL,
+                 transformation = "cohens_d", seed = 1, parallel = TRUE)
+

with priors_effect and priors_heterogeneity +corresponding to the +δ∼T(0,0.51,5)\delta \sim T(0,0.51,5) +and +τ∼InvGamma(1.79,0.28)\tau \sim InvGamma(1.79,0.28) +informed prior distributions for the “oral health” subfield and removing +the publication bias adjustment models by setting +priors_bias = NULL1^1. +Note that the package contains function NoBMA() from +version 3.1 which skips publication bias adjustment directly.

+

Alternatively, we can utilize the prior_informed +function that prepares informed prior distributions for the individual +medical subfields automatically.

+
+fit_BMA <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study,
+                 priors_effect        = prior_informed(name = "oral health", parameter = "effect", type = "smd"),
+                 priors_heterogeneity = prior_informed(name = "oral health", parameter = "heterogeneity", type = "smd"),
+                 priors_bias          = NULL,
+                 transformation = "cohens_d", seed = 1, parallel = TRUE)
+

The name argument specifies the medical subfield name +(use print(BayesTools::prior_informed_medicine_names) to +check names of all available subfields). The parameter +argument specifies whether we want prior distribution for the effect +size or heterogeneity. Finally, the type argument specifies +what type of measure we use for the meta-analysis (see +?prior_informed for more details regarding the informed +prior distributions).

+

We obtain the output with the summary function. Adding +the conditional = TRUE argument allows us to inspect the +conditional estimates, i.e., the effect size estimate assuming that the +models specifying the presence of the effect are true and the +heterogeneity estimates assuming that the models specifying the presence +of heterogeneity are +true2^2.

+
+summary(fit_BMA, conditional = TRUE)
+#> Call:
+#> RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, 
+#>     transformation = "cohens_d", priors_effect = prior_informed(name = "oral health", 
+#>         parameter = "effect", type = "smd"), priors_heterogeneity = prior_informed(name = "oral health", 
+#>         parameter = "heterogeneity", type = "smd"), priors_bias = NULL, 
+#>     parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/4       0.500       0.995      217.517
+#> Heterogeneity    2/4       0.500       0.778        3.511
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025 0.975
+#> mu  1.076  1.088 0.664 1.422
+#> tau 0.231  0.208 0.000 0.726
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+#> 
+#> Conditional estimates:
+#>      Mean Median 0.025 0.975
+#> mu  1.082  1.090 0.701 1.422
+#> tau 0.297  0.255 0.075 0.779
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

The output from the summary.RoBMA() function has 3 +parts. The first one under the ‘Robust Bayesian Meta-Analysis’ heading +provides a basic summary of the fitted models by component types +(presence of the Effect and Heterogeneity). The table summarizes the +prior and posterior probabilities and the inclusion Bayes factors of the +individual components. The results show that the inclusion Bayes factor +for the effect corresponds to the one reported in Bartoš et al. (2021), +BF10=218.53\text{BF}_{10} = 218.53 +and +BFrf=3.52\text{BF}_{\text{rf}} = 3.52 +(up to an MCMC error).

+

The second part under the ‘Model-averaged estimates’ heading displays +the parameter estimates model-averaged across all specified models +(i.e., including models specifying the effect size to be zero). We +ignore this section and move to the last part.

+

The third part under the ‘Conditional estimates’ heading displays the +conditional effect size estimate corresponding to the one reported in +Bartoš et al. (2021), +δ=1.082\delta = 1.082, +95% CI [0.686,1.412], and a heterogeneity estimate (not reported +previously).

+
+
+

Visualizing the Results +

+

The RoBMA package provides extensive options for +visualizing the results. Here, we visualize the prior (grey) and +posterior (black) distribution for the mean parameter.

+
+plot(fit_BMA, parameter = "mu", prior = TRUE)
+

+

By default, the function plots the model-averaged estimates across +all models; the arrows represent the probability of a spike, and the +lines represent the posterior density under models assuming non-zero +effect. The secondary y-axis (right) shows the probability of the spike +(at value 0) decreasing from 0.50, to 0.005 (also obtainable from the +‘Robust Bayesian Meta-Analysis’ field in summary.RoBMA() +function).

+

To visualize the conditional effect size estimate, we can add the +conditional = TRUE argument,

+
+plot(fit_BMA, parameter = "mu", prior = TRUE, conditional = TRUE)
+

+which displays only the model-averaged posterior distribution of the +effect size parameter for models assuming the presence of the +effect.

+

We can also visualize the estimates from the individual models used +in the ensemble. We do that with the plot_models() +function, which visualizes the effect size estimates and 95% CI of each +specified model included in the ensemble. Model 1 corresponds to the +fixed effect model assuming the absence of the effect, +H0fH_0^{\text{f}}, +Model 2 corresponds to the random effect model assuming the absence of +the effect, +H0rH_0^{\text{r}}, +Model 3 corresponds to the fixed effect model assuming the presence of +the effect, +H1fH_1^{\text{f}}, +and Model 4 corresponds to the random effect model assuming the presence +of the effect, +H1rH_1^{\text{r}}). +The size of the square representing the mean estimate reflects the +posterior model probability of the model, which is also displayed in the +right-hand side panel. The bottom part of the figure shows the model +averaged-estimate that is a combination of the individual model +posterior distributions weighted by the posterior model +probabilities.

+
+plot_models(fit_BMA)
+

+We see that the posterior model probability of the first two models +decreased to essentially zero (when rounding to two decimals), +completely omitting their estimates from the figure. Furthermore, the +much larger box of Model 4 (the random effect model assuming the +presence of the effect) shows that Model 4 received the largest share of +the posterior probability, +P(H1r)=0.77P(H_1^{\text{r}}) = 0.77)

+

The last type of visualization that we show here is the forest plot. +It displays the original studies’ effects and the meta-analytic estimate +within one figure. It can be requested by using the +forest() function. Here, we again set the +conditional = TRUE argument to display the conditional +model-averaged effect size estimate at the bottom.

+
+forest(fit_BMA, conditional = TRUE)
+

+

For more options provided by the plotting function, see its +documentation by using ?plot.RoBMA(), +?plot_models(), and ?forest().

+
+
+

Adjusting for Publication Bias with Robust Bayesian +Meta-Analysis +

+

Finally, we illustrate how to adjust our informed BMA for publication +bias with robust Bayesian meta-analysis Maier et +al. (2023). In short, we specify additional models assuming the +presence of the publication bias and correcting for it by either +specifying a selection model operating on +pp-values +(Vevea & Hedges, 1995) or by +specifying a publication bias adjustment method correcting for the +relationship between effect sizes and standard errors – PET-PEESE (Stanley, 2017; Stanley & Doucouliagos, +2014). See Bartoš et al. (2022) for +a tutorial.

+

To obtain a proper before and after publication bias adjustment +comparison, we fit the informed BMA model but using the default effect +size transformation (Fisher’s +zz).

+
+fit_BMAb <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study,
+                  priors_effect        = prior_informed(name = "oral health", parameter = "effect", type = "smd"),
+                  priors_heterogeneity = prior_informed(name = "oral health", parameter = "heterogeneity", type = "smd"),
+                  priors_bias          = NULL,
+                  seed = 1, parallel = TRUE)
+
+summary(fit_BMAb, conditional = TRUE)
+#> Call:
+#> RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, 
+#>     priors_effect = prior_informed(name = "oral health", parameter = "effect", 
+#>         type = "smd"), priors_heterogeneity = prior_informed(name = "oral health", 
+#>         parameter = "heterogeneity", type = "smd"), priors_bias = NULL, 
+#>     parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/4       0.500       0.997      347.932
+#> Heterogeneity    2/4       0.500       0.723        2.608
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025 0.975
+#> mu  1.045  1.052 0.705 1.344
+#> tau 0.186  0.163 0.000 0.623
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+#> 
+#> Conditional estimates:
+#>      Mean Median 0.025 0.975
+#> mu  1.048  1.053 0.720 1.344
+#> tau 0.256  0.220 0.064 0.681
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

We obtain noticeably stronger evidence for the presence of the +effect. This is a result of placing more weights on the fixed-effect +models, especially the fixed-effect model assuming the presence of the +effect +H1fH_1^f. +In our case, the increase in the posterior model probability of +H1fH_1^f +occurred because this model predicted the data slightly better after +removing the correlation between effect sizes and standard errors (a +consequence of using Fisher’s +zz +transformation). Nevertheless, the conditional effect size estimate +stayed almost the same.

+

Now, we fit the publication bias-adjusted model by simply removing +the priors_bias = NULL argument, which allows us to obtain +the default 36 models ensemble called RoBMA-PSMA (Bartoš et al., 2023).

+
+fit_RoBMA <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study,
+                   priors_effect        = prior_informed(name = "oral health", parameter = "effect", type = "smd"),
+                   priors_heterogeneity = prior_informed(name = "oral health", parameter = "heterogeneity", type = "smd"),
+                   seed = 1, parallel = TRUE)
+
+summary(fit_RoBMA, conditional = TRUE)
+#> Call:
+#> RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, 
+#>     priors_effect = prior_informed(name = "oral health", parameter = "effect", 
+#>         type = "smd"), priors_heterogeneity = prior_informed(name = "oral health", 
+#>         parameter = "heterogeneity", type = "smd"), parallel = TRUE, 
+#>     seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect         18/36       0.500       0.858        6.022
+#> Heterogeneity  18/36       0.500       0.714        2.502
+#> Bias           32/36       0.500       0.697        2.304
+#> 
+#> Model-averaged estimates:
+#>                    Mean Median 0.025  0.975
+#> mu                0.722  0.880 0.000  1.283
+#> tau               0.202  0.161 0.000  0.799
+#> omega[0,0.025]    1.000  1.000 1.000  1.000
+#> omega[0.025,0.05] 0.943  1.000 0.329  1.000
+#> omega[0.05,0.5]   0.874  1.000 0.071  1.000
+#> omega[0.5,0.95]   0.855  1.000 0.042  1.000
+#> omega[0.95,0.975] 0.866  1.000 0.050  1.000
+#> omega[0.975,1]    0.897  1.000 0.057  1.000
+#> PET               0.931  0.000 0.000  4.927
+#> PEESE             1.131  0.000 0.000 12.261
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+#> (Estimated publication weights omega correspond to one-sided p-values.)
+#> 
+#> Conditional estimates:
+#>                    Mean Median  0.025  0.975
+#> mu                0.838  0.938 -0.035  1.297
+#> tau               0.285  0.227  0.064  0.906
+#> omega[0,0.025]    1.000  1.000  1.000  1.000
+#> omega[0.025,0.05] 0.736  0.829  0.092  1.000
+#> omega[0.05,0.5]   0.411  0.373  0.014  0.951
+#> omega[0.5,0.95]   0.320  0.249  0.008  0.919
+#> omega[0.95,0.975] 0.376  0.311  0.009  0.958
+#> omega[0.975,1]    0.518  0.425  0.010  1.000
+#> PET               2.909  3.136  0.171  5.614
+#> PEESE             7.048  6.034  0.375 18.162
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+#> (Estimated publication weights omega correspond to one-sided p-values.)
+

We notice the additional values in the ‘Components summary’ table in +the ‘Bias’ row. The model is now extended with 32 publication bias +adjustment models that account for 50% of the prior model probability. +When comparing the RoBMA to the second BMA fit, we notice a large +decrease in the inclusion Bayes factor for the presence of the effect +BF10=6.02\text{BF}_{10} = 6.02 +vs. BF10=347.93\text{BF}_{10} = 347.93, +which still, however, presents moderate evidence for the presence of the +effect. We can further quantify the evidence in favor of the publication +bias with the inclusion Bayes factor for publication bias +BFpb=2.30\text{BF}_{pb} = 2.30, +which can be interpreted as weak evidence in favor of publication +bias.

+

We can also compare the publication bias unadjusted and publication +bias-adjusted conditional effect size estimates. Including models +assuming publication bias into our model-averaged estimate (assuming the +presence of the effect) slightly decreases the estimated effect to +δ=0.838\delta = 0.838, +95% CI [-0.035, 1.297] with a much wider confidence interval, as +visualized in the prior and posterior conditional effect size estimate +plot.

+
+plot(fit_RoBMA, parameter = "mu", prior = TRUE, conditional = TRUE)
+

+
+
+

Footnotes +

+

1^1 +The additional setting transformation = "cohens_d" allows +us to get more comparable results with the metaBMA R +package since RoBMA otherwise internally transforms the effect sizes to +Fisher’s +zz +for the fitting purposes. The seed = 1 and +parallel = TRUE options grant us exact reproducibility of +the results and parallelization of the fitting process.

+

2^2 +The model-averaged estimates that RoBMA returns by default +model-averaged across all specified models – a different behavior from +the metaBMA package that by default returns what we call +“conditional” estimates in RoBMA.

+
+
+

References +

+
+
+Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., & +Wagenmakers, E.-J. (2021). Bayesian model-averaged meta-analysis in +medicine. Statistics in Medicine, 40(30), 6743–6761. +https://doi.org/10.1002/sim.9170 +
+
+Bartoš, F., Maier, Maximilian, Quintana, D. S., & Wagenmakers, E.-J. +(2022). Adjusting for publication bias in JASP and +R — Selection models, PET-PEESE, +and robust Bayesian meta-analysis. Advances in Methods +and Practices in Psychological Science, 5(3), 1–19. https://doi.org/10.1177/25152459221109259 +
+
+Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & +Stanley, T. D. (2023). Robust Bayesian meta-analysis: +Model-averaging across complementary publication bias +adjustment methods. Research Synthesis Methods, 14(1), +99–116. https://doi.org/10.1002/jrsm.1594 +
+
+Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., & +Wagenmakers, E.-J. (2021). A primer on Bayesian +model-averaged meta-analysis. Advances in Methods and Practices in +Psychological Science, 4(3), 1–19. https://doi.org/10.1177/25152459211031256 +
+
+Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., +& Wagenmakers, E.-J. (2017). A Bayesian model-averaged +meta-analysis of the power pose effect with informed and default priors: +The case of felt power. Comprehensive Results in Social +Psychology, 2(1), 123–138. https://doi.org/10.1080/23743603.2017.1326760 +
+
+Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2023). Robust +Bayesian meta-analysis: Addressing publication +bias with model-averaging. Psychological Methods, +28(1), 107–122. https://doi.org/10.1037/met0000405 +
+
+Poulsen, S., Errboe, M., Mevil, Y. L., & Glenny, A.-M. (2006). +Potassium containing toothpastes for dentine hypersensitivity. +Cochrane Database of Systematic Reviews, 3. https://doi.org/10.1002/14651858.cd001476.pub2 +
+
+Stanley, T. D. (2017). Limitations of PET-PEESE and other +meta-analysis methods. Social Psychological and Personality +Science, 8(5), 581–591. https://doi.org/10.1177/1948550617693062 +
+
+Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression +approximations to reduce publication selection bias. Research +Synthesis Methods, 5(1), 60–78. https://doi.org/10.1002/jrsm.1095 +
+
+Vevea, J. L., & Hedges, L. V. (1995). A general linear model for +estimating effect size in the presence of publication bias. +Psychometrika, 60(3), 419–435. https://doi.org/10.1007/BF02294384 +
+
+
+
+
+ + + +
+

Developed by František Bartoš, Maximilian Maier.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
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Meta-Analysis with Binary Outcomes • RoBMA + + + + + + + + Skip to contents + + +

+ + + + +
+
+ +

Informed Bayesian Model-Averaged Meta-Analysis with Binary Outcomes

+

František +Bartoš

+ +

2024-12-11

+ + Source: vignettes/MedicineBiBMA.Rmd +
MedicineBiBMA.Rmd
+
+ + + +

Bayesian model-averaged meta-analysis can be specified using the +binomial likelihood and applied to data with dichotomous outcomes. This +vignette illustrates how to do this with an example from Bartoš et al. (2023), who implemented a +binomial-normal Bayesian model-averaged meta-analytic model and +developed informed prior distributions for meta-analyses of binary and +time-to-event outcomes based on the Cochrane database of systematic +reviews (see Bartoš et al. (2021) for +informed prior distributions for meta-analyses of continuous outcomes +highlighted in Informed Bayesian +Model-Averaged Meta-Analysis in Medicine vignette.

+
+

Binomial-Normal Bayesian Model-Averaged Meta-Analysis +

+

We illustrate how to fit the binomial-normal Bayesian model-averaged +meta-analysis using the RoBMA R package. For this purpose, +we reproduce the example of adverse effects of honey in treating acute +cough in children from Bartoš et al. +(2023), who reanalyzed two studies with adverse events of +nervousness, insomnia, or hyperactivity in the honey vs. no treatment +condition that were subjected to a meta-analysis by Oduwole et al. (2018).

+

We load the RoBMA package and specify the number of adverse events +and sample sizes in each arm as described on p. 73 (Oduwole et al., 2018).

+
+library(RoBMA)
+
+events_experimental        <- c(5, 2)
+events_control             <- c(0, 0)
+observations_experimental  <- c(35, 40)
+observations_control       <- c(39, 40)
+study_names <- c("Paul 2007", "Shadkam 2010")
+

Notice that both studies reported no adverse events in the control +group. Using a normal-normal meta-analytic model with log odds ratios +would require a continuity correction, which might result in bias. +Binomial-normal models allow us to circumvent the issue by modeling the +observed proportions directly (see Bartoš et al. +(2023) for more details).

+

First, we fit the binomial-normal Bayesian model-averaged +meta-analysis using informed prior distributions based on the +Acute Respiratory Infections subfield. We use the +BiBMA function and specify the observed events +(x1 and x2) and sample size (n1 +and n2) of adverse events and sample sizes in each arm. We +use the prior_informed function to specify the informed +prior distributions for the individual medical subfields +automatically.

+
+fit <- BiBMA(
+  x1          = events_experimental,
+  x2          = events_control,
+  n1          = observations_experimental,
+  n2          = observations_control,
+  study_names = study_names,
+  priors_effect        = prior_informed("Acute Respiratory Infections", type = "logOR", parameter = "effect"),
+  priors_heterogeneity = prior_informed("Acute Respiratory Infections", type = "logOR", parameter = "heterogeneity"),
+  seed = 1
+)
+

with priors_effect and priors_heterogeneity +corresponding to the +μ∼T(0,0.48,3)\mu \sim T(0,0.48,3) +and +τ∼InvGamma(1.67,0.45)\tau \sim InvGamma(1.67, 0.45) +prior distributions (see ?prior_informed for more details +regarding the informed prior distributions).

+

We obtain the output with the summary function. Adding +the conditional = TRUE argument allows us to inspect the +conditional estimates, i.e., the effect size estimate assuming that the +models specifying the presence of the effect are true, and the +heterogeneity estimates assuming that the models specifying the presence +of heterogeneity are true. We also set the +output_scale = "OR" argument to display the effect size +estimates on the odds ratio scale.

+
+summary(fit, conditional = TRUE, output_scale = "OR")
+#> Call:
+#> BiBMA(x1 = events_experimental, x2 = events_control, n1 = observations_experimental, 
+#>     n2 = observations_control, study_names = study_names, priors_effect = prior_informed("Acute Respiratory Infections", 
+#>         type = "logOR", parameter = "effect"), priors_heterogeneity = prior_informed("Acute Respiratory Infections", 
+#>         type = "logOR", parameter = "heterogeneity"), seed = 1)
+#> 
+#> Bayesian model-averaged meta-analysis (binomial-normal model)
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/4       0.500       0.725        2.630
+#> Heterogeneity    2/4       0.500       0.564        1.296
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025  0.975
+#> mu  3.389  1.642 0.842 15.143
+#> tau 0.420  0.158 0.000  2.594
+#> The effect size estimates are summarized on the OR scale and heterogeneity is summarized on the logOR scale (priors were specified on the log(OR) scale).
+#> 
+#> Conditional estimates:
+#>      Mean Median 0.025  0.975
+#> mu  4.242  2.261 0.781 17.613
+#> tau 0.747  0.426 0.097  3.233
+#> The effect size estimates are summarized on the OR scale and heterogeneity is summarized on the logOR scale (priors were specified on the log(OR) scale).
+

The output from the summary.RoBMA() function has three +parts. The first part, under the ‘Robust Bayesian Meta-Analysis’ heading +provides a basic summary of the fitted models by component types +(presence of the Effect and Heterogeneity). The results show that the +inclusion Bayes factor for the effect corresponds to the one reported in +Bartoš et al. (2023), +BF10=2.63\text{BF}_{10} = 2.63 +and +BFrf=1.30\text{BF}_{\text{rf}} = 1.30 +(up to an MCMC error)—weak/undecided evidence for the presence of the +effect and heterogeneity.

+

The second part, under the ‘Model-averaged estimates’ heading +displays the parameter estimates model-averaged across all specified +models (i.e., including models specifying the effect size to be zero). +These estimates are shrunk towards the null hypotheses of null effect or +no heterogeneity in accordance with the posterior uncertainty about the +presence of the effect or heterogeneity. We find the model-averaged mean +effect OR = 3.39, 95% CI [0.84, 15.14], and a heterogeneity estimate +τlogOR=0.42\tau_\text{logOR} = 0.42, +95% CI [0.00, 2.59].

+

The third part, under the ‘Conditional estimates’ heading displays +the conditional effect size and heterogeneity estimates (i.e., estimates +assuming presence of the effect or heterogeneity) corresponding to the +one reported in Bartoš et al. (2023), OR = +4.24, 95% CI [0.78, 17.61], and a heterogeneity estimate +τlogOR=0.75\tau_\text{logOR} = 0.75, +95% CI [0.10, 3.23].

+

We can also visualize the posterior distributions of the effect size +and heterogeneity parameters using the plot() function. +Here, we set the conditional = TRUE argument to display the +conditional effect size estimate and prior = TRUE to +include the prior distribution in the plot.

+
+plot(fit, parameter = "mu", prior = TRUE, conditional = TRUE)
+

+

Additional visualizations and summaries are demonstrated in the Reproducing BMA and Informed Bayesian Model-Averaged Meta-Analysis +in Medicine vignettes.

+
+
+

References +

+
+
+Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., & +Wagenmakers, E.-J. (2021). Bayesian model-averaged meta-analysis in +medicine. Statistics in Medicine, 40(30), 6743–6761. +https://doi.org/10.1002/sim.9170 +
+
+Bartoš, F., Otte, W. M., Gronau, Q. F., Timmers, B., Ly, A., & +Wagenmakers, E.-J. (2023). Empirical prior distributions for +Bayesian meta-analyses of binary and time-to-event +outcomes. https://doi.org/10.48550/arXiv.2306.11468 +
+
+Oduwole, O., Udoh, E. E., Oyo-Ita, A., & Meremikwu, M. M. (2018). +Honey for acute cough in children. Cochrane Database of Systematic +Reviews, 4. https://doi.org/10.1002/14651858.CD007094.pub5 +
+
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+ + + +
+

Developed by František Bartoš, Maximilian Maier.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+
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+ + + + +
+
+ +

Robust Bayesian Model-Averaged Meta-Regression

+

František +Bartoš

+ +

2024-12-11

+ + Source: vignettes/MetaRegression.Rmd +
MetaRegression.Rmd
+
+ + + +

Robust Bayesian model-averaged meta-regression (RoBMA-reg) extends +the robust Bayesian model-averaged meta-analysis (RoBMA) by including +covariates in the meta-analytic model. RoBMA-reg allows for estimating +and testing the moderating effects of study-level covariates on the +meta-analytic effect in a unified framework (e.g., accounting for +uncertainty in the presence vs. absence of the effect, heterogeneity, +and publication bias). This vignette illustrates how to fit a robust +Bayesian model-averaged meta-regression using the RoBMA R +package. We reproduce the example from Bartoš, +Maier, Stanley, et al. (2023), who re-analyzed a meta-analysis of +the effect of household chaos on child executive functions with the mean +age and assessment type covariates based on Andrews et al. (2021)’s meta-analysis.

+

First, we fit a frequentist meta-regression using the +metafor R package. Second, we explain the Bayesian +meta-regression model specification, the default prior distributions for +continuous and categorical moderators, and standardized effect sizes +input specification. Third, we estimate Bayesian model-averaged +meta-regression (without publication bias adjustment). Finally, we +estimate the complete robust Bayesian model-averaged +meta-regression.

+
+

Data +

+

We start by loading the Andrews2021 dataset included in +the RoBMA R package, which contains 36 estimates of the +effect of household chaos on child executive functions with the mean age +and assessment type covariates. The dataset includes correlation +coefficients (r), standard errors of the correlation +coefficients (se), the type of executive function +assessment (measure), and the mean age of the children +(age) in each study.

+
+library(RoBMA)
+data("Andrews2021", package = "RoBMA")
+head(Andrews2021)
+#>       r         se measure      age
+#> 1 0.070 0.04743416  direct 4.606660
+#> 2 0.033 0.04371499  direct 2.480833
+#> 3 0.170 0.10583005  direct 7.750000
+#> 4 0.208 0.08661986  direct 4.000000
+#> 5 0.270 0.02641969  direct 4.000000
+#> 6 0.170 0.05147815  direct 4.487500
+
+
+

Frequentist Meta-Regression +

+

We start by fitting a frequentist meta-regression using the +metafor R package (Wolfgang, +2010). While Andrews et al. (2021) +estimated univariate meta-regressions for each moderator, we directly +proceed by analyzing both moderators simultaneously. For consistency +with original reporting, we estimate the meta-regression using the +correlation coefficients and the standard errors provided by (Andrews et al., 2021); however, note that +Fisher’s z transformation is recommended for estimating meta-analytic +models (e.g., Stanley et al. (2024)).

+
+fit_rma <- metafor::rma(yi = r, sei = se, mods = ~ measure + age, data = Andrews2021)
+fit_rma
+#> 
+#> Mixed-Effects Model (k = 36; tau^2 estimator: REML)
+#> 
+#> tau^2 (estimated amount of residual heterogeneity):     0.0150 (SE = 0.0045)
+#> tau (square root of estimated tau^2 value):             0.1226
+#> I^2 (residual heterogeneity / unaccounted variability): 91.28%
+#> H^2 (unaccounted variability / sampling variability):   11.47
+#> R^2 (amount of heterogeneity accounted for):            15.24%
+#> 
+#> Test for Residual Heterogeneity:
+#> QE(df = 33) = 340.7613, p-val < .0001
+#> 
+#> Test of Moderators (coefficients 2:3):
+#> QM(df = 2) = 7.5445, p-val = 0.0230
+#> 
+#> Model Results:
+#> 
+#>                   estimate      se    zval    pval    ci.lb   ci.ub     
+#> intrcpt             0.0898  0.0467  1.9232  0.0545  -0.0017  0.1813   . 
+#> measureinformant    0.1202  0.0466  2.5806  0.0099   0.0289  0.2115  ** 
+#> age                 0.0030  0.0062  0.4867  0.6265  -0.0091  0.0151     
+#> 
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

The results reveal a statistically significant moderation effect of +the executive function assessment type on the effect of household chaos +on child executive functions +(p=0.0099p = 0.0099). +To explore the moderation effect further, we estimate the estimated +marginal means for the executive function assessment type using the +emmeans R package (Lenth et al., +2017).

+
+emmeans::emmeans(metafor::emmprep(fit_rma), specs = "measure")
+#>  measure   emmean     SE  df asymp.LCL asymp.UCL
+#>  direct     0.109 0.0305 Inf    0.0492     0.169
+#>  informant  0.229 0.0347 Inf    0.1612     0.297
+#> 
+#> Confidence level used: 0.95
+

Studies using the informant-completed questionnaires show a stronger +effect of household chaos on child executive functions, r = 0.229, 95% +CI [0.161, 0.297], than the direct assessment, r = 0.109, 95% CI [0.049, +0.169]; both types of studies show statistically significant +effects.

+

The mean age of the children does not significantly moderate the +effect +(p=0.627p = 0.627) +with the estimated regression coefficient of b = 0.003, 95% CI [-0.009, +0.015]. As usual, frequentist inference limits us to failing to reject +the null hypothesis. Here, we try to overcome this limitation with +Bayesian model-averaged meta-regression.

+
+
+

Bayesian Meta-Regression Specification +

+

Before we proceed with the Bayesian model-averaged meta-regression, +we provide a quick overview of the regression model specification. In +contrast to frequentist meta-regression, we need to specify prior +distributions on the regression coefficients, which encode the tested +hypotheses about the presence vs. absence of the moderation (specifying +different prior distributions corresponds to different hypotheses and +results in different conclusions). Importantly, the treatment of +continuous and categorical covariates differs in the Bayesian +model-averaged meta-regression.

+
+

Continuous vs. Categorical Moderators and Default Prior +Distributions +

+

The default prior distribution for continuous moderators is a normal +prior distribution with mean of 0 and a standard deviation of 1/4. In +other words, the default prior distribution assumes that the effect of +the moderator is small and smaller moderation effects are more likely +than larger effects. The default choice for continuous moderators can be +overridden by the prior_covariates argument (for all +continuous covariates) or by the priors argument (for +specific covariates, see ?RoBMA.reg for more information). +The package automatically standardizes the continuous moderators. This +achieves scale-invariance of the specified prior distributions and +ensures that the prior distribution for the intercept correspond to the +grand mean effect. This setting can be overridden by specifying the +standardize_predictors = FALSE argument.

+

The default prior distribution for the categorical moderators is a +normal distribution with a mean of 0 and a standard deviation of 1/4, +representing the deviation of each level from the grand mean effect. The +package uses standardized orthonormal contrasts +(contrast = "meandif") to model deviations of each category +from the grand mean effect. The default choice for categorical +moderators can be overridden by the prior_factors argument +(for all categorical covariates) or by the priors argument +(for specific covariates, see ?RoBMA.reg for more +information). The "meandif" contrasts achieve label +invariance (i.e., the coding of the categorical covariates does not +affect the results) and the prior distribution for the intercept +corresponds to the grand mean effect. Alternatively, the package also +allows specifying "treatment" contrasts, which result in a +prior distribution on the difference between the default level and the +remaining levels of the categorical covariate (with the intercept +corresponding to the effect in the default factor level).

+
+
+

Effect Size Input Specification +

+

Prior distributions for Bayesian meta-analyses are calibrated for the +standardized effect size measures. As such, the fitting function needs +to know what kind of effect size was supplied as the input. In +RoBMA() function, this is achieved by the d, +r, logOR, OR, z, +se, v, n, lCI, and +uCI arguments. The input is passed to the +combine_data() function in the background that combines the +effect sizes and merges them into a single data.frame. The +RoBMA.reg() (and NoBMA.reg()) function +requires the dataset to be passed as a data.frame (without missing +values) with column names identifying the - moderators passed using the +formula interface (i.e., ~ measure + age in our example) - +and the effect sizes and standard errors (i.e., r and +se in our example).

+

As such, it is crucial for the column names to correctly identify the +standardized effect sizes, standard errors, sample sizes, and +moderators.

+
+
+
+

Bayesian Model-Averaged Meta-Regression +

+

We fit the Bayesian model-averaged meta-regression using the +NoBMA.reg() function (the NoBMA.reg() function +is a wrapper around the RoBMA.reg() function that +automatically removes models adjusting for publication bias). We specify +the model formula with the ~ operator similarly to the +rma() function and pass the dataset as a data.frame with +named columns as outlined in the section above (the names need to +identify the moderators and effect size measures). We also set the +parallel = TRUE argument to speed up the computation by +running the chains in parallel and seed = 1 argument to +ensure reproducibility.

+
+fit_BMA <- NoBMA.reg(~ measure + age, data = Andrews2021, parallel = TRUE, seed = 1)
+

Note that the NoBMA.reg() function specifies the +combination of all models assuming presence vs. absence of the effect, +heterogeneity, moderation by measure, and moderation by +age, which corresponds to +2*2*2*2=162*2*2*2=16 +models. Including each additional moderator doubles the number of +models, leading to an exponential increase in model count and +significantly longer fitting times.

+

Once the ensemble is estimated, we can use the summary() +functions with the output_scale = "r" argument, which +produces meta-analytic estimates that are transformed to the correlation +scale.

+
+summary(fit_BMA, output_scale = "r")
+#> Call:
+#> RoBMA.reg(formula = formula, data = data, test_predictors = test_predictors, 
+#>     study_names = study_names, study_ids = study_ids, transformation = transformation, 
+#>     prior_scale = prior_scale, standardize_predictors = standardize_predictors, 
+#>     effect_direction = "positive", priors = priors, model_type = model_type, 
+#>     priors_effect = priors_effect, priors_heterogeneity = priors_heterogeneity, 
+#>     priors_bias = NULL, priors_effect_null = priors_effect_null, 
+#>     priors_heterogeneity_null = priors_heterogeneity_null, priors_bias_null = prior_none(), 
+#>     priors_hierarchical = priors_hierarchical, priors_hierarchical_null = priors_hierarchical_null, 
+#>     prior_covariates = prior_covariates, prior_covariates_null = prior_covariates_null, 
+#>     prior_factors = prior_factors, prior_factors_null = prior_factors_null, 
+#>     chains = chains, sample = sample, burnin = burnin, adapt = adapt, 
+#>     thin = thin, parallel = parallel, autofit = autofit, autofit_control = autofit_control, 
+#>     convergence_checks = convergence_checks, save = save, seed = seed, 
+#>     silent = silent)
+#> 
+#> Bayesian model-averaged meta-regression (normal-normal model)
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect          8/16       0.500       1.000 6.637645e+05
+#> Heterogeneity   8/16       0.500       1.000 3.439130e+40
+#> 
+#> Meta-regression components summary:
+#>         Models Prior prob. Post. prob. Inclusion BF
+#> measure   8/16       0.500       0.826        4.739
+#> age       8/16       0.500       0.197        0.245
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025 0.975
+#> mu  0.163  0.163 0.118 0.208
+#> tau 0.121  0.120 0.086 0.167
+#> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale).
+#> 
+#> Model-averaged meta-regression estimates:
+#>                            Mean Median  0.025 0.975
+#> intercept                 0.163  0.163  0.118 0.208
+#> measure [dif: direct]    -0.047 -0.051 -0.099 0.000
+#> measure [dif: informant]  0.047  0.051  0.000 0.099
+#> age                       0.003  0.000 -0.011 0.043
+#> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale).
+

The summary function produces output with multiple sections The first +section contains the Components summary with the hypothesis +test results for the overall effect size and heterogeneity. We find +overwhelming evidence for both with inclusion Bayes factors +(Inclusion BF) above 10,000.

+

The second section contains the +Meta-regression components summary with the hypothesis test +results for the moderators. We find moderate evidence for the moderation +by the executive function assessment type, +BFmeasure=4.74\text{BF}_{\text{measure}} = 4.74. +Furthermore, we find moderate evidence for the null hypothesis of no +moderation by mean age of the children, +BFage=0.245\text{BF}_{\text{age}} = 0.245 +(i.e., BF for the null is +1/0.245=4.081/0.245 = 4.08). +These findings extend the frequentist meta-regression by disentangling +the absence of evidence from the evidence of absence.

+

The third section contains the Model-averaged estimates +with the model-averaged estimates for mean effect +ρ=0.16\rho = 0.16, +95% CI [0.12, 0.21] and between-study heterogeneity +τFisher’s z=0.12\tau_{\text{Fisher's z}} = 0.12, +95% CI [0.09, 0.17].

+

The fourth section contains the +Model-averaged meta-regression estimates with the +model-averaged regression coefficient estimates. The main difference +from the usual frequentist meta-regression output is that the +categorical predictors are summarized as a difference from the grand +mean for each factor level. Here, the intercept regression +coefficient estimate corresponds to the grand mean effect and the +measure [dif: direct] regression coefficient estimate of +-0.047, 95% CI [-0.099, 0.000] corresponds to the difference between the +direct assessment and the grand mean. As such, the results suggest that +the effect size in studies using direct assessment is lower in +comparison to the grand mean of the studies. The age +regression coefficient estimate is standardized, therefore, the increase +of 0.003, 95% CI [-0.011, 0.043] corresponds to the increase in the mean +effect when increasing mean age of children by one standard +deviation.

+

Similarly to the frequentist meta-regression, we can use the +marginal_summary() function to obtain the marginal +estimates for each of the factor levels.

+
+marginal_summary(fit_BMA, output_scale = "r")
+#> Call:
+#> RoBMA.reg(formula = formula, data = data, test_predictors = test_predictors, 
+#>     study_names = study_names, study_ids = study_ids, transformation = transformation, 
+#>     prior_scale = prior_scale, standardize_predictors = standardize_predictors, 
+#>     effect_direction = "positive", priors = priors, model_type = model_type, 
+#>     priors_effect = priors_effect, priors_heterogeneity = priors_heterogeneity, 
+#>     priors_bias = NULL, priors_effect_null = priors_effect_null, 
+#>     priors_heterogeneity_null = priors_heterogeneity_null, priors_bias_null = prior_none(), 
+#>     priors_hierarchical = priors_hierarchical, priors_hierarchical_null = priors_hierarchical_null, 
+#>     prior_covariates = prior_covariates, prior_covariates_null = prior_covariates_null, 
+#>     prior_factors = prior_factors, prior_factors_null = prior_factors_null, 
+#>     chains = chains, sample = sample, burnin = burnin, adapt = adapt, 
+#>     thin = thin, parallel = parallel, autofit = autofit, autofit_control = autofit_control, 
+#>     convergence_checks = convergence_checks, save = save, seed = seed, 
+#>     silent = silent)
+#> 
+#> Robust Bayesian meta-analysis
+#> Model-averaged marginal estimates:
+#>                     Mean Median 0.025 0.975 Inclusion BF
+#> intercept          0.163  0.163 0.118 0.208          Inf
+#> measure[direct]    0.117  0.116 0.052 0.185       50.151
+#> measure[informant] 0.208  0.210 0.130 0.280          Inf
+#> age[-1SD]          0.160  0.161 0.106 0.208          Inf
+#> age[0SD]           0.163  0.163 0.118 0.208          Inf
+#> age[1SD]           0.166  0.165 0.117 0.220          Inf
+#> The estimates are summarized on the correlation scale (priors were specified on the Cohen's d scale).
+#> mu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.
+#> mu_measure[informant]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.
+#> mu_age[-1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.
+#> mu_age[0SD]: There is a considerable cluster of prior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid.
+#> mu_age[0SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.
+#> mu_age[1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.
+

The estimated marginal means are similar to the frequentist results. +Studies using the informant-completed questionnaires again show a +stronger effect of household chaos on child executive functions, +ρ=0.208\rho = 0.208, +95% CI [0.130, 0.280], than the direct assessment, +ρ=0.117\rho = 0.117, +95% CI [0.052, 0.185].

+

The last column summarizes results from a test against a null +hypothesis of marginal means equals 0. Here, we find very strong +evidence for the effect size of studies using the informant-completed +questionnaires differing from zero, +BF10=50.1\text{BF}_{10} = 50.1 +and extreme evidence for the effect size of studies using the direct +assessment differing from zero, +BF10=∞\text{BF}_{10} = \infty. +The test is performed using the change from prior to posterior +distribution at 0 (i.e., the Savage-Dickey density ratio) assuming the +presence of the overall effect or the presence of difference according +to the tested factor. Because the tests use prior and posterior samples, +calculating the Bayes factor can be problematic when the posterior +distribution is far from the tested value. In such cases, warning +messages are printed and +BF10=∞\text{BF}_{10} = \infty +returned (like here)—while the actual Bayes factor is less than +infinity, it is still too large to be computed precisely given the +posterior samples.

+

The full model-averaged posterior marginal means distribution can be +visualized by the marginal_plot() function.

+
+marginal_plot(fit_BMA, parameter = "measure", output_scale = "r", lwd = 2)
+

+
+
+

Robust Bayesian Model-Averaged Meta-Regression +

+

Finally, we adjust the Bayesian model-averaged meta-regression model +by fitting the robust Bayesian model-averaged meta-regression. In +contrast to the previous publication bias unadjusted model ensemble, +RoBMA-reg extends the model ensemble by the publication bias component +specified via 6 weight functions and PET-PEESE (Bartoš, Maier, Wagenmakers, et al., 2023). We +use the RoBMA.reg() function with the same arguments as in +the previous section. The estimation time further increases as the +ensemble now contains 144 models.

+
+fit_RoBMA <- RoBMA.reg(~ measure + age, data = Andrews2021, parallel = TRUE, seed = 1)
+
+summary(fit_RoBMA, output_scale = "r")
+#> Call:
+#> RoBMA.reg(formula = ~measure + age, data = Andrews2021, chains = 1, 
+#>     parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-regression
+#> Components summary:
+#>                Models Prior prob. Post. prob. Inclusion BF
+#> Effect         72/144       0.500       0.334 5.020000e-01
+#> Heterogeneity  72/144       0.500       1.000 1.043816e+23
+#> Bias          128/144       0.500       0.965 2.795800e+01
+#> 
+#> Meta-regression components summary:
+#>         Models Prior prob. Post. prob. Inclusion BF
+#> measure 72/144       0.500       0.950       19.086
+#> age     72/144       0.500       0.154        0.182
+#> 
+#> Model-averaged estimates:
+#>                    Mean Median 0.025  0.975
+#> mu                0.031  0.000 0.000  0.164
+#> tau               0.106  0.104 0.074  0.147
+#> omega[0,0.025]    1.000  1.000 1.000  1.000
+#> omega[0.025,0.05] 0.999  1.000 1.000  1.000
+#> omega[0.05,0.5]   0.998  1.000 1.000  1.000
+#> omega[0.5,0.95]   0.997  1.000 1.000  1.000
+#> omega[0.95,0.975] 0.997  1.000 1.000  1.000
+#> omega[0.975,1]    0.997  1.000 1.000  1.000
+#> PET               2.056  2.494 0.000  3.293
+#> PEESE             1.916  0.000 0.000 19.068
+#> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale).
+#> (Estimated publication weights omega correspond to one-sided p-values.)
+#> 
+#> Model-averaged meta-regression estimates:
+#>                            Mean Median  0.025 0.975
+#> intercept                 0.031  0.000  0.000 0.164
+#> measure [dif: direct]    -0.063 -0.064 -0.106 0.000
+#> measure [dif: informant]  0.063  0.064  0.000 0.106
+#> age                       0.000  0.000 -0.024 0.022
+#> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale).
+

All previously described functions for manipulating the fitted model +work identically with the publication bias adjusted model. As such, we +just briefly mention the main differences found after adjusting for +publication bias.

+

RoBMA-reg reveals strong evidence of publication bias +BFpb=28.0\text{BF}_{\text{pb}} = 28.0. +Furthermore, accounting for publication bias turns the previously found +evidence for the overall effect into a weak evidence against the effect +BF10=0.50\text{BF}_{10} = 0.50 +and notably reduces the mean effect estimate +ρ=0.031\rho = 0.031, +95% CI [0.000, 0.164].

+
+marginal_summary(fit_RoBMA, output_scale = "r")
+#> Call:
+#> RoBMA.reg(formula = ~measure + age, data = Andrews2021, chains = 1, 
+#>     parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Model-averaged marginal estimates:
+#>                      Mean Median  0.025 0.975 Inclusion BF
+#> intercept           0.031  0.000  0.000 0.164        0.516
+#> measure[direct]    -0.031 -0.056 -0.105 0.121        0.575
+#> measure[informant]  0.093  0.077  0.000 0.223        7.643
+#> age[-1SD]           0.031  0.000 -0.015 0.163        0.732
+#> age[0SD]            0.031  0.000  0.000 0.164        1.013
+#> age[1SD]            0.031  0.000 -0.024 0.168        0.743
+#> The estimates are summarized on the correlation scale (priors were specified on the Cohen's d scale).
+#> mu_age[0SD]: There is a considerable cluster of prior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid.
+

The estimated marginal means now suggest that studies using the +informant-completed questionnaires show a much smaller effect of +household chaos on child executive functions, +ρ=0.093\rho = 0.093, +95% CI [0.000, 0.223] with only moderate evidence against no effect, +BF10=7.64\text{BF}_{10} = 7.64, +while studies using direct assessment even provide weak evidence against +the effect of household chaos on child executive functions, +BF10=0.58\text{BF}_{10} = 0.58, +with most likely effect sizes around zero, +ρ=−0.031\rho = -0.031, +95% CI [-0.105, 0.121].

+

A visual summary of the estimated marginal means highlights the +considerably wider model-averaged posterior distributions of the +marginal means—a consequence of accounting and adjusting for publication +bias.

+
+marginal_plot(fit_RoBMA, parameter = "measure", output_scale = "r", lwd = 2)
+

+

The Bayesian model-averaged meta-regression models are compatible +with the remaining custom specification, visualization, and summary +functions included in the RoBMA R package, highlighted in +other vignettes. E.g., custom model specification is demonstrated in the +vignette Fitting Custom Meta-Analytic +Ensembles and visualizations and summaries are demonstrated in the +Reproducing BMA and Informed Bayesian Model-Averaged Meta-Analysis +in Medicine vignettes.

+
+
+

References +

+
+
+Andrews, K., Atkinson, L., Harris, M., & Gonzalez, A. (2021). +Examining the effects of household chaos on child executive functions: +A meta-analysis. Psychological Bulletin, +147(1), 16–32. https://doi.org/10.1037/bul0000311 +
+
+Bartoš, F., Maier, M., Stanley, T., & Wagenmakers, E.-J. (2023). +Robust Bayesian +meta-regression—Model-averaged moderation analysis in the +presence of publication bias. https://doi.org/10.31234/osf.io/98xb5 +
+
+Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & +Stanley, T. D. (2023). Robust Bayesian meta-analysis: +Model-averaging across complementary publication bias +adjustment methods. Research Synthesis Methods, 14(1), +99–116. https://doi.org/10.1002/jrsm.1594 +
+
+Lenth, R. V., Bolker, B., Buerkner, P., Giné-Vázquez, I., Herve, M., +Jung, M., Love, J., Miguez, F., Riebl, H., & Singmann, H. (2017). +emmeans: Estimated marginal +means, aka least-squares means. https://cran.r-project.org/package=emmeans +
+
+Stanley, T., Doucouliagos, H., Maier, M., & Bartoš, F. (2024). +Correcting bias in the meta-analysis of correlations. Psychological +Methods. https://doi.org/10.1037/met0000662 +
+
+Wolfgang, V. (2010). Conducting meta-analyses in R with the +metafor package. Journal of Statistical +Software, 36(3), 1–48. https://www.jstatsoft.org/v36/i03/ +
+
+
+
+
+ + + +
+

Developed by František Bartoš, Maximilian Maier.

+
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Site built with pkgdown 2.1.1.

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a/docs/articles/ReproducingBMA.html b/docs/articles/ReproducingBMA.html new file mode 100644 index 00000000..fd937e01 --- /dev/null +++ b/docs/articles/ReproducingBMA.html @@ -0,0 +1,364 @@ + + + + + + + +Reproducing Bayesian Model-Averaged Meta-Analysis • RoBMA + + + + + + + + Skip to contents + + +

+ + + + +
+
+ +

Reproducing Bayesian Model-Averaged Meta-Analysis

+

František +Bartoš

+ +

2024-12-11

+ + Source: vignettes/ReproducingBMA.Rmd +
ReproducingBMA.Rmd
+
+ + + +

By default, the RoBMA package estimates an ensemble of 36 +meta-analytic models and provides functions for convenient manipulation +of the fitted object. However, the package has been designed so it can +be used as a framework for estimating any combination of meta-analytic +models (or a single model). Here, we illustrate how to build a custom +ensemble of meta-analytic models - specifically the same ensemble that +is used in ‘classical’ Bayesian Model-Averaged Meta-Analysis (Bartoš et al., 2021; Gronau et al., 2017, +2021). See this vignette if +you are interested in building more customized ensembles or Bartoš et al. (2022) for a tutorial on fitting +(custom) models in JASP.

+
+

Reproducing Bayesian Model-Averaged Meta-Analysis (BMA) +

+

We illustrate how to fit a classical BMA (not adjusting for +publication bias) using RoBMA. For this purpose, we +reproduce a meta-analysis of registered reports on Power posing by Gronau et al. (2017). We focus only on the +analysis of all reported results using a Cauchy prior distribution with +scale +1/21/\sqrt{2} +for the effect size estimation (half-Cauchy for testing) and +inverse-gamma distribution with shape = 1 and scale = 0.15 for the +heterogeneity parameter. You can find the figure from the original +publication here +and the paper’s supplementary materials at https://osf.io/fxg32/.

+

First, we load the power posing data provided within the metaBMA +package and reproduce the analysis performed by Gronau et al. (2017).

+
+data("power_pose", package = "metaBMA")
+power_pose[,c("study", "effectSize", "SE")]
+#>                study effectSize        SE
+#> 1      Bailey et al.  0.2507640 0.2071399
+#> 2       Ronay et al.  0.2275180 0.1931046
+#> 3 Klaschinski et al.  0.3186069 0.1423228
+#> 4     Bombari et al.  0.2832082 0.1421356
+#> 5        Latu et al.  0.1463949 0.1416107
+#> 6      Keller et al.  0.1509773 0.1221166
+
+fit_BMA_test <- metaBMA::meta_bma(y   = power_pose$effectSize, SE = power_pose$SE,
+                                  d   = metaBMA::prior(family = "halfcauchy", param = 1/sqrt(2)),
+                                  tau = metaBMA::prior(family = "invgamma", param = c(1, .15)))
+ 
+fit_BMA_est  <- metaBMA::meta_bma(y   = power_pose$effectSize, SE = power_pose$SE,
+                                  d   = metaBMA::prior(family = "cauchy", param = c(0, 1/sqrt(2))),
+                                  tau = metaBMA::prior(family = "invgamma", param = c(1, .15)))
+
+fit_BMA_test$inclusion
+#> ### Inclusion Bayes factor ###
+#>       Model Prior Posterior included
+#> 1  fixed_H0  0.25   0.00868         
+#> 2  fixed_H1  0.25   0.77745        x
+#> 3 random_H0  0.25   0.02061         
+#> 4 random_H1  0.25   0.19325        x
+#> 
+#>   Inclusion posterior probability: 0.971 
+#>   Inclusion Bayes factor: 33.136
+
+round(fit_BMA_est$estimates,2)
+#>          mean   sd 2.5%  50% 97.5% hpd95_lower hpd95_upper  n_eff Rhat
+#> averaged 0.22 0.06 0.09 0.22  0.34        0.09        0.34     NA   NA
+#> fixed    0.22 0.06 0.10 0.22  0.34        0.10        0.34 3026.5    1
+#> random   0.22 0.08 0.07 0.22  0.37        0.07        0.37 6600.4    1
+

From the output, we can see the inclusion Bayes factor for the effect +size was +BF10=33.14BF_{10} = 33.14 +and the effect size estimate 0.22, 95% HDI [0.09, 0.34], which matches +the reported results. Please note that the metaBMA package +model-averages only across the +H1H_{1} +models, whereas the RoBMA package model-averages across all +models (assuming the presence and absence of the effect).

+
+
+

Using RoBMA +

+

Now we reproduce the analysis with RoBMA. We set the +corresponding prior distributions for effect sizes +(μ\mu) +and heterogeneity +(τ\tau), +and remove the alternative prior distributions for the publication bias +by setting priors_bias = NULL. To specify the half-Cauchy +prior distribution with the RoBMA::prior() function we use +a regular Cauchy distribution and truncate it at zero (note that both +metaBMA and RoBMA export their own +prior() functions that will clash when loading both +packages simultaneously). The inverse-gamma prior distribution for the +heterogeneity parameter is the default option (we specify it for +completeness). We omit the specifications for the null prior +distributions for the effect size, heterogeneity (both of which are set +to a spike at 0 by default), and publication bias (which is set to no +publication bias by default). Note that starting from version 3.1, the +package includes the NoBMA() function, which allows users +to skip publication bias adjustment directly.

+

Since metaBMA model-averages the effect size estimates +only across the models assuming presence of the effect, we remove the +models assuming absence of the effect from the estimation ensemble with +priors_effect_null = NULL. Finally, we set +transformation = "cohens_d" to estimate the models on +Cohen’s d scale. RoBMA uses Fisher’s z scale by +default and transforms the estimated coefficients back to the scale that +is used for specifying the prior distributions. We speed up the +computation by setting parallel = TRUE, and set a seed for +reproducibility.

+
+library(RoBMA)
+
+fit_RoBMA_test <- RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study,
+                        priors_effect  = prior(
+                          distribution = "cauchy",
+                          parameters = list(location = 0, scale = 1/sqrt(2)),
+                          truncation = list(0, Inf)),
+                        priors_heterogeneity = prior(
+                          distribution = "invgamma",
+                          parameters = list(shape = 1, scale = 0.15)),
+                        priors_bias = NULL,
+                        transformation = "cohens_d", seed = 1, parallel = TRUE)
+
+fit_RoBMA_est  <- RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study,
+                        priors_effect  = prior(
+                          distribution = "cauchy",
+                          parameters = list(location = 0, scale = 1/sqrt(2))),
+                        priors_heterogeneity = prior(
+                          distribution = "invgamma",
+                          parameters = list(shape = 1, scale = 0.15)),
+                        priors_bias = NULL,
+                        priors_effect_null = NULL,
+                        transformation = "cohens_d", seed = 2, parallel = TRUE)
+
+summary(fit_RoBMA_test)
+#> Call:
+#> RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study, 
+#>     transformation = "cohens_d", priors_effect = prior(distribution = "cauchy", 
+#>         parameters = list(location = 0, scale = 1/sqrt(2)), truncation = list(0, 
+#>             Inf)), priors_heterogeneity = prior(distribution = "invgamma", 
+#>         parameters = list(shape = 1, scale = 0.15)), priors_bias = NULL, 
+#>     parallel = TRUE, seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/4       0.500       0.971       33.112
+#> Heterogeneity    2/4       0.500       0.214        0.273
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025 0.975
+#> mu  0.213  0.217 0.000 0.348
+#> tau 0.022  0.000 0.000 0.178
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+
+summary(fit_RoBMA_est)
+#> Call:
+#> RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study, 
+#>     transformation = "cohens_d", priors_effect = prior(distribution = "cauchy", 
+#>         parameters = list(location = 0, scale = 1/sqrt(2))), 
+#>     priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1, 
+#>         scale = 0.15)), priors_bias = NULL, priors_effect_null = NULL, 
+#>     parallel = TRUE, seed = 2)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect           2/2       1.000       1.000          Inf
+#> Heterogeneity    1/2       0.500       0.200        0.250
+#> 
+#> Model-averaged estimates:
+#>      Mean Median 0.025 0.975
+#> mu  0.220  0.220 0.096 0.346
+#> tau 0.019  0.000 0.000 0.152
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+

The output from the summary.RoBMA() function has 2 +parts. The first one under the “Robust Bayesian Meta-Analysis” heading +provides a basic summary of the fitted models by component types +(presence of the Effect and Heterogeneity). The table summarizes the +prior and posterior probabilities and the inclusion Bayes factors of the +individual components. The results for the half-Cauchy model specified +for testing show that the inclusion BF is nearly identical to the one +computed by the metaBMA package, +BF10=33.11\text{BF}_{10} = 33.11.

+

The second part under the ‘Model-averaged estimates’ heading displays +the parameter estimates. The results for the unrestricted Cauchy model +specified for estimation show the effect size estimate +μ=0.22\mu = 0.22, +95% CI [0.10, 0.35] that also mirrors the one obtained from +metaBMA package.

+
+
+

Visualizing the Results +

+

RoBMA provides extensive options for visualizing the results. Here, +we visualize the prior (grey) and posterior (black) distribution for the +mean parameter.

+
+plot(fit_RoBMA_est, parameter = "mu", prior = TRUE, xlim = c(-1, 1))
+

+

If we visualize the effect size from the model specified for testing, +we notice a few more things. The function plots the model-averaged +estimates across all models by default, including models assuming the +absence of the effect. The arrows represents the probability of a spike, +here, at the value 0. The secondary y-axis (right) shows the probability +of the value 0 decreased from 0.50, to 0.03 (also obtainable from the +“Robust Bayesian Meta-Analysis” field in the +summary.RoBMA() function). Furthermore, the continuous +prior distributions for the effect size under the alternative hypothesis +are truncated to only positive values, reflecting the assumption that +the effect size cannot be negative.

+
+plot(fit_RoBMA_test, parameter = "mu", prior = TRUE, xlim = c(-.5, 1))
+

+

We can also visualize the estimates from the individual models used +in the ensemble. We do that with the plot_models() +function, which visualizes the effect size estimates and 95% CI of each +of the specified models from the estimation ensemble (Model 1 +corresponds to the fixed effect model and Model 2 to the random effect +model). The size of the square representing the mean estimate reflects +the posterior model probability of the model, which is also displayed in +the right-hand side panel. The bottom part of the figure shows the +model-averaged estimate that is a combination of the individual model +posterior distributions weighted by the posterior model +probabilities.

+
+plot_models(fit_RoBMA_est)
+

+

The last type of visualization that we show here is the forest plot. +It displays both the effect sizes from the original studies and the +overall meta-analytic estimate in a single figure. It can be requested +by using the forest() function.

+
+forest(fit_RoBMA_est)
+

+

For more options provided by the plotting function, see its +documentation using ?plot.RoBMA(), +?plot_models(), and ?forest().

+
+
+

References +

+
+
+Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., & +Wagenmakers, E.-J. (2021). Bayesian model-averaged meta-analysis in +medicine. Statistics in Medicine, 40(30), 6743–6761. +https://doi.org/10.1002/sim.9170 +
+
+Bartoš, F., Maier, Maximilian, Quintana, D. S., & Wagenmakers, E.-J. +(2022). Adjusting for publication bias in JASP and +R — Selection models, PET-PEESE, +and robust Bayesian meta-analysis. Advances in Methods +and Practices in Psychological Science, 5(3), 1–19. https://doi.org/10.1177/25152459221109259 +
+
+Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., & +Wagenmakers, E.-J. (2021). A primer on Bayesian +model-averaged meta-analysis. Advances in Methods and Practices in +Psychological Science, 4(3), 1–19. https://doi.org/10.1177/25152459211031256 +
+
+Gronau, Q. F., Van Erp, S., Heck, D. W., Cesario, J., Jonas, K. J., +& Wagenmakers, E.-J. (2017). A Bayesian model-averaged +meta-analysis of the power pose effect with informed and default priors: +The case of felt power. Comprehensive Results in Social +Psychology, 2(1), 123–138. https://doi.org/10.1080/23743603.2017.1326760 +
+
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+ + + +
+

Developed by František Bartoš, Maximilian Maier.

+
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Site built with pkgdown 2.1.1.

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+ + + + +
+
+ +

Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis

+

František +Bartoš, Maximilian Maier, Daniel S. Quintana & Eric-Jan +Wagenmakers

+ +

2022

+ + Source: vignettes/Tutorial.Rmd +
Tutorial.Rmd
+
+ + + +

This R markdown file accompanies the tutorial Adjusting for +publication bias in JASP and R: Selection models, PET-PEESE, and robust +Bayesian meta-analysis published in Advances in Methods and +Practices in Psychological Science (Bartoš +et al., 2022).

+

The following R-markdown file illustrates how to:

+
    +
  • Load a CSV file into R,
    • +
  • Transform effect sizes,
    • +
  • Perform a random effect meta-analysis,
    • +
  • Adjust for publication bias with: +
      +
    • PET-PEESE (Stanley, 2017; Stanley & +Doucouliagos, 2014),
      • +
    • Selection models (Iyengar & Greenhouse, +1988; Vevea & Hedges, 1995),
      • +
    • Robust Bayesian meta-analysis (RoBMA) (Bartoš +et al., 2023; Maier et al., 2023).
      • +
    +
    • +
+

See the full paper for additional details regarding the data set, +methods, and interpretation.

+
+

Set-up +

+

Before we start, we need to install JAGS (which is +needed for installation of the RoBMA package) and the R +packages that we use in the analysis. Specifically the +RoBMA, weightr, and metafor R +packages.

+

JAGS can be downloaded from the JAGS website. +Subsequently, we install the R packages with the +install.packages() function.

+

{r install.packages(c("RoBMA", "weightr", "metafor")) If +you happen to use the new M1 Mac machines with Apple silicon, see this +blogpost outlining how to install JAGS on M1. In short, you will +have to install Intel version of R (Intel/x86-64) from CRAN, not the Arm64 +(Apple silicon) version. Note that there might have been some changes in +the installation process since the blogpost was written and there might +be a JAGS version compatible with Apple silicon available now.

+

Once all of the packages are installed, we can load them into the +workspace with the library() function.

+ +
+
+

Lui (2015) +

+

Lui (2015) studied how the +acculturation mismatch (AM) that is the result of the contrast between +the collectivist cultures of Asian and Latin immigrant groups and the +individualist culture in the United States correlates with +intergenerational cultural conflict (ICC). Lui +(2015) meta-analyzed 18 independent studies correlating AM with +ICC. A standard reanalysis indicates a significant effect of AM on +increased ICC, r = 0.250, p < .001.

+
+

Data manipulation +

+

First, we load the Lui2015.csv file into R with the +read.csv() function and inspect the first six data entries +with the head() function (the data set is also included in +the package and can be accessed via the +data("Lui2015", package = "RoBMA") call).

+
+df <- read.csv(file = "Lui2015.csv")
+
+head(df)
+#>      r   n                   study
+#> 1 0.21 115 Ahn, Kim, & Park (2008)
+#> 2 0.29 283   Basanez et al. (2013)
+#> 3 0.22  80         Bounkeua (2007)
+#> 4 0.26 109        Hajizadeh (2009)
+#> 5 0.23  61            Hamid (2007)
+#> 6 0.54 107    Hwang & Wood (2009a)
+

We see that the data set contains three columns. The first column +called r contains the effect sizes coded as correlation +coefficients, the second column called n contains the +sample sizes, and the third column called study contains +names of the individual studies.

+

We can access the individual variables using the data set name and +the dollar ($) sign followed by the name of the column. For +example, we can print all of the effect sizes with the df$r +command.

+
+df$r
+#>  [1]  0.21  0.29  0.22  0.26  0.23  0.54  0.56  0.29  0.26  0.02 -0.06  0.38
+#> [13]  0.25  0.08  0.17  0.33  0.36  0.13
+

The printed output shows that the data set contains mostly positive +effect sizes with the largest correlation coefficient r = 0.54.

+
+
+

Effect size transformations +

+

Before we start analyzing the data, we transform the effect sizes +from correlation coefficients +ρ\rho +to Fisher’s z. Correlation coefficients are not well suited for +meta-analysis because (1) they are bounded to a range (-1, 1) with +non-linear increases near the boundaries and (2) the standard error of +the correlation coefficients is related to the effect size. Fisher’s +z transformation mitigates both issues. It unwinds the (-1, 1) +range to +(−∞-\infty, +∞\infty), +makes the sampling distribution approximately normal, and breaks the +dependency between standard errors and effect sizes.

+

To apply the transformation, we use the combine_data() +function from the RoBMA package. We pass the correlation +coefficients into the r argument, the sample sizes to the +n argument, and set the transformation +argument to "fishers_z" (the study_names +argument is optional). The function combine_data() then +saves the transformed effect size estimates into a data frame called +dfz, where the y column corresponds to +Fisher’s z transformation of the correlation coefficient and +se column corresponds to the standard error of Fisher’s +z.

+
+dfz <- combine_data(r = df$r, n = df$n, study_names = df$study, transformation = "fishers_z")
+head(dfz)
+#>           y         se             study_names study_ids weight
+#> 1 0.2131713 0.09449112 Ahn, Kim, & Park (2008)        NA     NA
+#> 2 0.2985663 0.05976143   Basanez et al. (2013)        NA     NA
+#> 3 0.2236561 0.11396058         Bounkeua (2007)        NA     NA
+#> 4 0.2661084 0.09712859        Hajizadeh (2009)        NA     NA
+#> 5 0.2341895 0.13130643            Hamid (2007)        NA     NA
+#> 6 0.6041556 0.09805807    Hwang & Wood (2009a)        NA     NA
+

We can also transform the effect sizes according to Cohen’s +d transformation (which we utilize later to fit the selection +models).

+
+dfd <- combine_data(r = df$r, n = df$n, study_names = df$study, transformation = "cohens_d")
+head(dfd)
+#>           y        se             study_names study_ids weight
+#> 1 0.4295790 0.1886397 Ahn, Kim, & Park (2008)        NA     NA
+#> 2 0.6060437 0.1215862   Basanez et al. (2013)        NA     NA
+#> 3 0.4510508 0.2264322         Bounkeua (2007)        NA     NA
+#> 4 0.5385205 0.1950065        Hajizadeh (2009)        NA     NA
+#> 5 0.4726720 0.2596249            Hamid (2007)        NA     NA
+#> 6 1.2831708 0.2123140    Hwang & Wood (2009a)        NA     NA
+
+
+

Re-analysis with random effect meta-analysis +

+

We now estimate a random effect meta-analysis with the +rma() function imported from the metafor +package (Wolfgang, 2010) and verify that +we arrive at the same results as reported in the Lui (2015) paper. The yi argument +is used to pass the column name containing effect sizes, the +sei argument is used to pass the column name containing +standard errors, and the data argument is used to pass the +data frame containing both variables.

+
+fit_rma <- rma(yi = y, sei = se, data = dfz)
+fit_rma
+#> 
+#> Random-Effects Model (k = 18; tau^2 estimator: REML)
+#> 
+#> tau^2 (estimated amount of total heterogeneity): 0.0229 (SE = 0.0107)
+#> tau (square root of estimated tau^2 value):      0.1513
+#> I^2 (total heterogeneity / total variability):   77.79%
+#> H^2 (total variability / sampling variability):  4.50
+#> 
+#> Test for Heterogeneity:
+#> Q(df = 17) = 73.5786, p-val < .0001
+#> 
+#> Model Results:
+#> 
+#> estimate      se    zval    pval   ci.lb   ci.ub      
+#>   0.2538  0.0419  6.0568  <.0001  0.1717  0.3359  *** 
+#> 
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

Indeed, we find that the effect size estimate from the random effect +meta-analysis corresponds to the one reported in Lui (2015). It is important to remember that we +used Fisher’s z to estimate the models; therefore, the +estimated results are on the Fisher’s z scale. To transform the +effect size estimate to the correlation coefficients, we can use the +z2r() function from the RoBMA package,

+
+z2r(fit_rma$b)
+#>              [,1]
+#> intrcpt 0.2484877
+

Transforming the effect size estimate results in the correlation +coefficient +ρ\rho += 0.25.

+
+
+
+

PET-PEESE +

+

The first publication bias adjustment that we perform is PET-PEESE. +PET-PEESE adjusts for the relationship between effect sizes and standard +errors. To our knowledge, PET-PEESE is not currently implemented in any +R-package. However, since PET and PEESE are weighted regressions of +effect sizes on standard errors (PET) or standard errors squared +(PEESE), we can estimate both PET and PEESE models with the +lm() function. Inside the lm() function call, +we specify that y is the response variable (left hand side +of the ~ sign) and se is the predictor (the +right-hand side). Furthermore, we specify the weights +argument that allows us to weight the meta-regression by inverse +variance and set the data = dfz argument, which specifies +that all of the variables come from the transformed, dfz, +data set.

+
+fit_PET <- lm(y ~ se, weights = 1/se^2, data = dfz)
+summary(fit_PET)
+#> 
+#> Call:
+#> lm(formula = y ~ se, data = dfz, weights = 1/se^2)
+#> 
+#> Weighted Residuals:
+#>     Min      1Q  Median      3Q     Max 
+#> -3.8132 -0.9112 -0.0139  0.5166  3.3151 
+#> 
+#> Coefficients:
+#>               Estimate Std. Error t value Pr(>|t|)  
+#> (Intercept) -0.0008722  0.1081247  -0.008   0.9937  
+#> se           2.8549650  1.3593450   2.100   0.0519 .
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual standard error: 1.899 on 16 degrees of freedom
+#> Multiple R-squared:  0.2161, Adjusted R-squared:  0.1671 
+#> F-statistic: 4.411 on 1 and 16 DF,  p-value: 0.05192
+

The summary() function allows us to explore details of +the fitted model. The (Intercept) coefficient refers to the +meta-analytic effect size (corrected for the correlation with standard +errors). Again, it is important to keep in mind that the effect size +estimate is on the Fisher’s z scale. We obtain the estimate on +correlation scale with the z2r() function (we pass the +estimated effect size using the +summary(fit_PET)$coefficients["(Intercept)", "Estimate"] +command, which extracts the estimate from the fitted model, it is +equivalent to simply pasting the value directly +z2r(-0.0008722083)).

+
+z2r(summary(fit_PET)$coefficients["(Intercept)", "Estimate"])
+#> [1] -0.000872208
+

Since the Fisher’s z transformation is almost linear around +zero, we obtain an almost identical estimate.

+

More importantly, since the test for the effect size with PET was not +significant at +α=.10\alpha = .10, +we interpret the PET model. However, if the test for effect size were +significant, we would fit and interpret the PEESE model. The PEESE model +can be fitted in an analogous way, by replacing the predictor of +standard errors with standard errors squared (we need to wrap the +se^2 predictor in I() that tells R to square +the predictor prior to fitting the model).

+
+fit_PEESE <- lm(y ~ I(se^2), weights = 1/se^2, data = dfz)
+summary(fit_PEESE)
+#> 
+#> Call:
+#> lm(formula = y ~ I(se^2), data = dfz, weights = 1/se^2)
+#> 
+#> Weighted Residuals:
+#>     Min      1Q  Median      3Q     Max 
+#> -3.7961 -0.9581 -0.1156  0.6718  3.4608 
+#> 
+#> Coefficients:
+#>             Estimate Std. Error t value Pr(>|t|)  
+#> (Intercept)  0.11498    0.06201   1.854   0.0822 .
+#> I(se^2)     15.58064    7.96723   1.956   0.0682 .
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual standard error: 1.927 on 16 degrees of freedom
+#> Multiple R-squared:  0.1929, Adjusted R-squared:  0.1425 
+#> F-statistic: 3.824 on 1 and 16 DF,  p-value: 0.06821
+
+
+

Selection models +

+

The second publication bias adjustment that we will perform is +selection models. Selection models adjust for the different publication +probabilities in different p-value intervals. Selection models +are implemented in weightr package +(weightfunct() function; Coburn et +al. (2019)) and newly also in the metafor package +(selmodel() function; Wolfgang +(2010)). First, we use the weightr implementation +and fit the “4PSM” selection model that specifies three distinct +p-value intervals: (1) covering the range of significant +p-values for effect sizes in the expected direction +(0.00-0.025), (2) covering the range of “marginally” significant +p-values for effect sizes in the expected direction +(0.025-0.05), and (3) covering the range of non-significant +p-values (0.05-1). We use Cohen’s d transformation of +the correlation coefficients since it is better at maintaining the +distribution of test statistics. To fit the model, we need to pass the +effect sizes (dfd$y) into the effect argument +and variances (dfd$se^2) into the v argument +(note that we need to pass the vector of values directly since the +weightfunct() function does not allow us to pass the data +frame directly as did the previous functions). We further set +steps = c(0.025, 0.05) to specify the appropriate +cut-points (note that the steps correspond to one-sided +p-values), and we set table = TRUE to obtain the +frequency of p values in each of the specified intervals.

+
+fit_4PSM <- weightfunct(effect = dfd$y, v = dfd$se^2, steps = c(0.025, 0.05), table = TRUE)
+#> Warning in weightfunct(effect = dfd$y, v = dfd$se^2, steps = c(0.025, 0.05), :
+#> At least one of the p-value intervals contains three or fewer effect sizes,
+#> which may lead to estimation problems. Consider re-specifying the cutpoints.
+fit_4PSM
+#> 
+#> Unadjusted Model (k = 18):
+#> 
+#> tau^2 (estimated amount of total heterogeneity): 0.0920 (SE = 0.0423)
+#> tau (square root of estimated tau^2 value):  0.3034
+#> 
+#> Test for Heterogeneity:
+#> Q(df = 17) = 75.4999, p-val = 5.188348e-09
+#> 
+#> Model Results:
+#> 
+#>           estimate std.error z-stat      p-val ci.lb  ci.ub
+#> Intercept    0.516   0.08473   6.09 1.1283e-09  0.35 0.6821
+#> 
+#> Adjusted Model (k = 18):
+#> 
+#> tau^2 (estimated amount of total heterogeneity): 0.1289 (SE = 0.0682)
+#> tau (square root of estimated tau^2 value):  0.3590
+#> 
+#> Test for Heterogeneity:
+#> Q(df = 17) = 75.4999, p-val = 5.188348e-09
+#> 
+#> Model Results:
+#> 
+#>                  estimate std.error z-stat   p-val   ci.lb  ci.ub
+#> Intercept          0.2675    0.2009 1.3311 0.18316 -0.1264 0.6613
+#> 0.025 < p < 0.05   0.5008    0.5449 0.9191 0.35803 -0.5671 1.5688
+#> 0.05 < p < 1       0.1535    0.1570 0.9777 0.32821 -0.1542 0.4611
+#> 
+#> Likelihood Ratio Test:
+#> X^2(df = 2) = 3.844252, p-val = 0.1463
+#> 
+#> Number of Effect Sizes per Interval:
+#> 
+#>                         Frequency
+#> p-values <0.025                14
+#> 0.025 < p-values < 0.05         1
+#> 0.05 < p-values < 1             3
+

Note the warning message informing us about the fact that our data do +not contain a sufficient number of p-values in one of the +p-value intervals. The model output obtained by printing the +fitted model object fit_4PSM shows that there is only one +p-value in the (0.025, 0.05) interval. We can deal with this +issue by joining the “marginally” significant and non-significant +p-value interval, resulting in the “3PSM” model.

+
+fit_3PSM <- weightfunct(effect = dfd$y, v = dfd$se^2, steps = c(0.025), table = TRUE)
+fit_3PSM
+#> 
+#> Unadjusted Model (k = 18):
+#> 
+#> tau^2 (estimated amount of total heterogeneity): 0.0920 (SE = 0.0423)
+#> tau (square root of estimated tau^2 value):  0.3034
+#> 
+#> Test for Heterogeneity:
+#> Q(df = 17) = 75.4999, p-val = 5.188348e-09
+#> 
+#> Model Results:
+#> 
+#>           estimate std.error z-stat      p-val ci.lb  ci.ub
+#> Intercept    0.516   0.08473   6.09 1.1283e-09  0.35 0.6821
+#> 
+#> Adjusted Model (k = 18):
+#> 
+#> tau^2 (estimated amount of total heterogeneity): 0.1148 (SE = 0.0577)
+#> tau (square root of estimated tau^2 value):  0.3388
+#> 
+#> Test for Heterogeneity:
+#> Q(df = 17) = 75.4999, p-val = 5.188348e-09
+#> 
+#> Model Results:
+#> 
+#>               estimate std.error z-stat    p-val     ci.lb  ci.ub
+#> Intercept       0.3220    0.1676  1.921 0.054698 -0.006484 0.6504
+#> 0.025 < p < 1   0.2275    0.2004  1.135 0.256293 -0.165324 0.6204
+#> 
+#> Likelihood Ratio Test:
+#> X^2(df = 1) = 3.107176, p-val = 0.077948
+#> 
+#> Number of Effect Sizes per Interval:
+#> 
+#>                      Frequency
+#> p-values <0.025             14
+#> 0.025 < p-values < 1         4
+

The new model does not suffer from the estimation problem due to the +limited number of p-values in the intervals, so we can now +interpret the results with more confidence. First, we check the test for +heterogeneity that clearly rejects the null hypothesis +Q(df = 17) = 75.4999, $p$ = 5.188348e-09 (if we did not +find evidence for heterogeneity, we could have proceeded by fitting the +fixed effects version of the model by specifying the +fe = TRUE argument). We follow by checking the test for +publication bias which is a likelihood ratio test comparing the +unadjusted and adjusted estimate +X^2(df = 1) = 3.107176, $p$ = 0.077948. The result of the +test is slightly ambiguous – we would reject the null hypothesis of no +publication bias with +α=0.10\alpha = 0.10 +but not with +α=0.05\alpha = 0.05.

+

If we decide to interpret the estimated effect size, we have to again +transform it back to the correlation scale. However, this time we need +to use the d2r() function since we supplied the effect +sizes as Cohen’s d (note that the effect size estimate +corresponds to the second value in the fit_3PSM$adj_est +object for the random effect model, alternatively, we could simply use +d2r(0.3219641)).

+
+d2r(fit_3PSM$adj_est[2])
+#> [1] 0.1589358
+

Alternatively, we could have conducted the analysis analogously but +with the metafor package. First, we would fit a random +effect meta-analysis with the Cohen’s d transformed effect +sizes.

+
+fit_rma_d <- rma(yi = y, sei = se, data = dfd)
+

Subsequently, we would have used the selmodel function, +passing the estimated random effect meta-analysis object and specifying +the type = "stepfun" argument to obtain a step weight +function and setting the appropriate steps with the +steps = c(0.025) argument.

+
+fit_sel_d <- selmodel(fit_rma_d, type = "stepfun", steps = c(0.025))
+fit_sel_d
+#> 
+#> Random-Effects Model (k = 18; tau^2 estimator: ML)
+#> 
+#> tau^2 (estimated amount of total heterogeneity): 0.1148 (SE = 0.0577)
+#> tau (square root of estimated tau^2 value):      0.3388
+#> 
+#> Test for Heterogeneity:
+#> LRT(df = 1) = 32.7499, p-val < .0001
+#> 
+#> Model Results:
+#> 
+#> estimate      se    zval    pval    ci.lb   ci.ub    
+#>   0.3220  0.1676  1.9214  0.0547  -0.0065  0.6504  . 
+#> 
+#> Test for Selection Model Parameters:
+#> LRT(df = 1) = 3.1072, p-val = 0.0779
+#> 
+#> Selection Model Results:
+#> 
+#>                      k  estimate      se     zval    pval   ci.lb   ci.ub      
+#> 0     < p <= 0.025  14    1.0000     ---      ---     ---     ---     ---      
+#> 0.025 < p <= 1       4    0.2275  0.2004  -3.8537  0.0001  0.0000  0.6204  *** 
+#> 
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

The output verifies the results obtained in the previous +analysis.

+
+
+

Robust Bayesian meta-analysis +

+

The third and final publication bias adjustment that we will perform +is robust Bayesian meta-analysis (RoBMA). RoBMA uses Bayesian +model-averaging to combine inference from both PET-PEESE and selection +models. We use the RoBMA R package (and the +RoBMA() function; Bartoš & Maier +(2020)) to fit the default 36 model ensemble (called RoBMA-PSMA) +based on an orthogonal combination of models assuming the presence and +absence of the effect size, heterogeneity, and publication bias. The +models assuming the presence of publication bias are further split into +six weight function models and models utilizing the PET and PEESE +publication bias adjustment. To fit the model, we can directly pass the +original correlation coefficients into the r argument and +sample sizes into the n argument – the RoBMA() +function will internally transform them to the Fisher’s z scale +and, by default, return the estimates on a Cohen’s d scale +which is used to specify the prior distributions (both of these settings +can be changed with the prior_scale and +transformation arguments, and the output can be +conveniently transformed later). We further set the model +argument to "PSMA" to fit the 36 model ensemble and use the +seed argument to make the analysis reproducible (it uses +MCMC sampling in contrast to the previous methods). We turn on parallel +estimation by setting the parallel = TRUE argument (the +parallel processing might in some cases fail, try rerunning the model +one more time or turning the parallel processing off in that case).

+
+fit_RoBMA <- RoBMA(r = df$r, n = df$n, seed = 1, model = "PSMA", parallel = TRUE)
+

This step can take some time depending on your CPU. For example, this +will take approximately 1 minute on a fast CPU (e.g., AMD Ryzen 3900x +12c/24t) and up to ten minutes or longer on slower CPUs (e.g., 2.7 GHz +Intel Core i5).

+

We use the summary() function to explore details of the +fitted model.

+
+summary(fit_RoBMA)
+#> Call:
+#> RoBMA(r = df$r, n = df$n, model_type = "PSMA", parallel = TRUE, 
+#>     save = "min", seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect         18/36       0.500       0.552        1.232
+#> Heterogeneity  18/36       0.500       1.000    19168.311
+#> Bias           32/36       0.500       0.845        5.436
+#> 
+#> Model-averaged estimates:
+#>                    Mean Median  0.025  0.975
+#> mu                0.195  0.087 -0.008  0.598
+#> tau               0.330  0.307  0.166  0.597
+#> omega[0,0.025]    1.000  1.000  1.000  1.000
+#> omega[0.025,0.05] 0.936  1.000  0.438  1.000
+#> omega[0.05,0.5]   0.740  1.000  0.065  1.000
+#> omega[0.5,0.95]   0.697  1.000  0.028  1.000
+#> omega[0.95,0.975] 0.704  1.000  0.028  1.000
+#> omega[0.975,1]    0.713  1.000  0.028  1.000
+#> PET               0.828  0.000  0.000  3.291
+#> PEESE             0.802  0.000  0.000 10.805
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+#> (Estimated publication weights omega correspond to one-sided p-values.)
+

The printed output consists of two parts. The first table called +Components summary contains information about the fitted +models. It tells us that we estimated the ensemble with 18/36 models +assuming the presence of an effect, 18/36 models assuming the presence +of heterogeneity, and 32/36 models assuming the presence of the +publication bias. The second column summarizes the prior model +probabilities of models assuming either presence of the individual +components – here, we see that the presence and absence of the +components are balanced a priori. The third column contains information +about the posterior probability of models assuming the presence of the +components – we can observe that the posterior model probabilities of +models assuming the presence of an effect slightly increased to 0.552. +The last column contains information about the evidence in favor of the +presence of any of those components. Evidence for the presence of an +effect is undecided; the models assuming the presence of an effect are +only 1.232 times more likely given the data than the models assuming the +absence of an effect. However, we find overwhelming evidence in favor of +heterogeneity, with the models assuming the presence of heterogeneity +being 19,168 times more likely given the data than models assuming the +absence of heterogeneity, and moderate evidence in favor of publication +bias.

+

As the name indicates, the second table called +Model-averaged estimates contains information about the +model-averaged estimates. The first row labeled mu +corresponds to the model-averaged effect size estimate (on Cohen’s +d scale) and the second row labeled tau +corresponds to the model-averaged heterogeneity estimates. Below are the +estimated model-averaged weights for the different p-value +intervals and the PET and PEESE regression coefficients. We convert the +estimates to the correlation coefficients by adding the +output_scale = "r" argument to the summary function.

+
+summary(fit_RoBMA, output_scale = "r")
+#> Call:
+#> RoBMA(r = df$r, n = df$n, model_type = "PSMA", parallel = TRUE, 
+#>     save = "min", seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect         18/36       0.500       0.552        1.232
+#> Heterogeneity  18/36       0.500       1.000    19168.311
+#> Bias           32/36       0.500       0.845        5.436
+#> 
+#> Model-averaged estimates:
+#>                    Mean Median  0.025  0.975
+#> mu                0.095  0.043 -0.004  0.286
+#> tau               0.165  0.154  0.083  0.299
+#> omega[0,0.025]    1.000  1.000  1.000  1.000
+#> omega[0.025,0.05] 0.936  1.000  0.438  1.000
+#> omega[0.05,0.5]   0.740  1.000  0.065  1.000
+#> omega[0.5,0.95]   0.697  1.000  0.028  1.000
+#> omega[0.95,0.975] 0.704  1.000  0.028  1.000
+#> omega[0.975,1]    0.713  1.000  0.028  1.000
+#> PET               0.828  0.000  0.000  3.291
+#> PEESE             1.603  0.000  0.000 21.610
+#> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale).
+#> (Estimated publication weights omega correspond to one-sided p-values.)
+

Now, we have obtained the model-averaged effect size estimate on the +correlation scale. If we were interested in the estimates +model-averaging only across the models assuming the presence of an +effect (for the effect size estimate), heterogeneity (for the +heterogeneity estimate), and publication bias (for the publication bias +weights and PET and PEESE regression coefficients), we could have added +the conditional = TRUE argument to the summary function. A +quick textual summary of the model can also be generated with the +interpret() function.

+
+interpret(fit_RoBMA, output_scale = "r")
+#> [1] "Robust Bayesian meta-analysis found weak evidence in favor of the effect, BF_10 = 1.23, with mean model-averaged estimate correlation = 0.095, 95% CI [-0.004,  0.286]. Robust Bayesian meta-analysis found strong evidence in favor of the heterogeneity, BF^rf = 19168.31, with mean model-averaged estimate tau = 0.165, 95% CI [0.083, 0.299]. Robust Bayesian meta-analysis found moderate evidence in favor of the publication bias, BF_pb = 5.44."
+

We can also obtain summary information about the individual models by +specifying the type = "models" option. The resulting table +shows the prior and posterior model probabilities and inclusion Bayes +factors for the individual models (we also set the +short_name = TRUE argument reducing the width of the output +by abbreviating names of the prior distributions).

+
+summary(fit_RoBMA, type = "models", short_name = TRUE)
+#> Call:
+#> RoBMA(r = df$r, n = df$n, model_type = "PSMA", parallel = TRUE, 
+#>     save = "min", seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Models overview:
+#>  Model Prior Effect Prior Heterogeneity
+#>      1         S(0)                S(0)
+#>      2         S(0)                S(0)
+#>      3         S(0)                S(0)
+#>      4         S(0)                S(0)
+#>      5         S(0)                S(0)
+#>      6         S(0)                S(0)
+#>      7         S(0)                S(0)
+#>      8         S(0)                S(0)
+#>      9         S(0)                S(0)
+#>     10         S(0)         Ig(1, 0.15)
+#>     11         S(0)         Ig(1, 0.15)
+#>     12         S(0)         Ig(1, 0.15)
+#>     13         S(0)         Ig(1, 0.15)
+#>     14         S(0)         Ig(1, 0.15)
+#>     15         S(0)         Ig(1, 0.15)
+#>     16         S(0)         Ig(1, 0.15)
+#>     17         S(0)         Ig(1, 0.15)
+#>     18         S(0)         Ig(1, 0.15)
+#>     19      N(0, 1)                S(0)
+#>     20      N(0, 1)                S(0)
+#>     21      N(0, 1)                S(0)
+#>     22      N(0, 1)                S(0)
+#>     23      N(0, 1)                S(0)
+#>     24      N(0, 1)                S(0)
+#>     25      N(0, 1)                S(0)
+#>     26      N(0, 1)                S(0)
+#>     27      N(0, 1)                S(0)
+#>     28      N(0, 1)         Ig(1, 0.15)
+#>     29      N(0, 1)         Ig(1, 0.15)
+#>     30      N(0, 1)         Ig(1, 0.15)
+#>     31      N(0, 1)         Ig(1, 0.15)
+#>     32      N(0, 1)         Ig(1, 0.15)
+#>     33      N(0, 1)         Ig(1, 0.15)
+#>     34      N(0, 1)         Ig(1, 0.15)
+#>     35      N(0, 1)         Ig(1, 0.15)
+#>     36      N(0, 1)         Ig(1, 0.15)
+#>                   Prior Bias                 Prior prob. log(marglik)
+#>                                                    0.125       -74.67
+#>            omega[2s: .05] ~ CumD(1, 1)             0.010       -49.60
+#>        omega[2s: .1, .05] ~ CumD(1, 1, 1)          0.010       -47.53
+#>            omega[1s: .05] ~ CumD(1, 1)             0.010       -41.70
+#>      omega[1s: .05, .025] ~ CumD(1, 1, 1)          0.010       -38.03
+#>        omega[1s: .5, .05] ~ CumD(1, 1, 1)          0.010       -44.41
+#>  omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1)       0.010       -40.79
+#>                       PET ~ C(0, 1)[0, Inf]        0.031        -5.01
+#>                     PEESE ~ C(0, 5)[0, Inf]        0.031       -12.17
+#>                                                    0.125        -6.95
+#>            omega[2s: .05] ~ CumD(1, 1)             0.010        -5.96
+#>        omega[2s: .1, .05] ~ CumD(1, 1, 1)          0.010        -5.09
+#>            omega[1s: .05] ~ CumD(1, 1)             0.010         2.72
+#>      omega[1s: .05, .025] ~ CumD(1, 1, 1)          0.010         2.93
+#>        omega[1s: .5, .05] ~ CumD(1, 1, 1)          0.010         2.91
+#>  omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1)       0.010         3.30
+#>                       PET ~ C(0, 1)[0, Inf]        0.031         3.62
+#>                     PEESE ~ C(0, 5)[0, Inf]        0.031         1.62
+#>                                                    0.125       -13.17
+#>            omega[2s: .05] ~ CumD(1, 1)             0.010       -13.10
+#>        omega[2s: .1, .05] ~ CumD(1, 1, 1)          0.010       -12.87
+#>            omega[1s: .05] ~ CumD(1, 1)             0.010       -12.75
+#>      omega[1s: .05, .025] ~ CumD(1, 1, 1)          0.010       -12.86
+#>        omega[1s: .5, .05] ~ CumD(1, 1, 1)          0.010       -13.29
+#>  omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1)       0.010       -13.25
+#>                       PET ~ C(0, 1)[0, Inf]        0.031        -7.07
+#>                     PEESE ~ C(0, 5)[0, Inf]        0.031        -7.58
+#>                                                    0.125         1.79
+#>            omega[2s: .05] ~ CumD(1, 1)             0.010         1.75
+#>        omega[2s: .1, .05] ~ CumD(1, 1, 1)          0.010         2.16
+#>            omega[1s: .05] ~ CumD(1, 1)             0.010         3.11
+#>      omega[1s: .05, .025] ~ CumD(1, 1, 1)          0.010         3.01
+#>        omega[1s: .5, .05] ~ CumD(1, 1, 1)          0.010         2.98
+#>  omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1)       0.010         3.06
+#>                       PET ~ C(0, 1)[0, Inf]        0.031         2.75
+#>                     PEESE ~ C(0, 5)[0, Inf]        0.031         2.55
+#>  Post. prob. Inclusion BF
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.001
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.001
+#>        0.000        0.001
+#>        0.033        3.231
+#>        0.041        4.025
+#>        0.040        3.919
+#>        0.059        5.927
+#>        0.243        9.957
+#>        0.033        1.055
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.000        0.000
+#>        0.155        1.287
+#>        0.012        1.201
+#>        0.019        1.822
+#>        0.048        4.831
+#>        0.044        4.347
+#>        0.043        4.223
+#>        0.046        4.617
+#>        0.102        3.504
+#>        0.083        2.797
+

To obtain a summary of the individual model diagnostics, we set the +type = "diagnostics" argument. The resulting table provides +information about the maximum MCMC error, relative MCMC error, minimum +ESS, and maximum R-hat when aggregating over the parameters of each +model. As we can see, we obtain acceptable ESS and R-hat diagnostic +values.

+
+summary(fit_RoBMA, type = "diagnostics")
+#> Call:
+#> RoBMA(r = df$r, n = df$n, model_type = "PSMA", parallel = TRUE, 
+#>     save = "min", seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Diagnostics overview:
+#>  Model Prior Effect Prior Heterogeneity
+#>      1     Spike(0)            Spike(0)
+#>      2     Spike(0)            Spike(0)
+#>      3     Spike(0)            Spike(0)
+#>      4     Spike(0)            Spike(0)
+#>      5     Spike(0)            Spike(0)
+#>      6     Spike(0)            Spike(0)
+#>      7     Spike(0)            Spike(0)
+#>      8     Spike(0)            Spike(0)
+#>      9     Spike(0)            Spike(0)
+#>     10     Spike(0)   InvGamma(1, 0.15)
+#>     11     Spike(0)   InvGamma(1, 0.15)
+#>     12     Spike(0)   InvGamma(1, 0.15)
+#>     13     Spike(0)   InvGamma(1, 0.15)
+#>     14     Spike(0)   InvGamma(1, 0.15)
+#>     15     Spike(0)   InvGamma(1, 0.15)
+#>     16     Spike(0)   InvGamma(1, 0.15)
+#>     17     Spike(0)   InvGamma(1, 0.15)
+#>     18     Spike(0)   InvGamma(1, 0.15)
+#>     19 Normal(0, 1)            Spike(0)
+#>     20 Normal(0, 1)            Spike(0)
+#>     21 Normal(0, 1)            Spike(0)
+#>     22 Normal(0, 1)            Spike(0)
+#>     23 Normal(0, 1)            Spike(0)
+#>     24 Normal(0, 1)            Spike(0)
+#>     25 Normal(0, 1)            Spike(0)
+#>     26 Normal(0, 1)            Spike(0)
+#>     27 Normal(0, 1)            Spike(0)
+#>     28 Normal(0, 1)   InvGamma(1, 0.15)
+#>     29 Normal(0, 1)   InvGamma(1, 0.15)
+#>     30 Normal(0, 1)   InvGamma(1, 0.15)
+#>     31 Normal(0, 1)   InvGamma(1, 0.15)
+#>     32 Normal(0, 1)   InvGamma(1, 0.15)
+#>     33 Normal(0, 1)   InvGamma(1, 0.15)
+#>     34 Normal(0, 1)   InvGamma(1, 0.15)
+#>     35 Normal(0, 1)   InvGamma(1, 0.15)
+#>     36 Normal(0, 1)   InvGamma(1, 0.15)
+#>                          Prior Bias                         max[error(MCMC)]
+#>                                                                           NA
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)                0.00024
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)             0.00295
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)                0.00014
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)             0.00326
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)             0.00033
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)          0.00309
+#>                              PET ~ Cauchy(0, 1)[0, Inf]              0.00236
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]              0.01223
+#>                                                                      0.00118
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)                0.00296
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)             0.00295
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)                0.00110
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)             0.00331
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)             0.00357
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)          0.00307
+#>                              PET ~ Cauchy(0, 1)[0, Inf]              0.00454
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]              0.02470
+#>                                                                      0.00038
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)                0.00303
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)             0.00290
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)                0.00309
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)             0.00278
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)             0.00332
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)          0.00293
+#>                              PET ~ Cauchy(0, 1)[0, Inf]              0.03247
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]              0.05228
+#>                                                                      0.00090
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)                0.00308
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)             0.00293
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)                0.00477
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)             0.00340
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)             0.00543
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)          0.00499
+#>                              PET ~ Cauchy(0, 1)[0, Inf]              0.04070
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]              0.07238
+#>  max[error(MCMC)/SD] min(ESS) max(R-hat)
+#>                   NA       NA         NA
+#>                0.016     4158      1.000
+#>                0.016     3793      1.000
+#>                0.015     4622      1.000
+#>                0.017     3357      1.000
+#>                0.017     3509      1.001
+#>                0.018     3064      1.001
+#>                0.010     9917      1.001
+#>                0.010     9589      1.000
+#>                0.010     9632      1.001
+#>                0.013     5518      1.002
+#>                0.015     4565      1.001
+#>                0.015     4395      1.001
+#>                0.015     4502      1.002
+#>                0.018     3206      1.001
+#>                0.017     3480      1.001
+#>                0.012     7342      1.001
+#>                0.012     7051      1.000
+#>                0.010     9712      1.001
+#>                0.013     5522      1.000
+#>                0.015     4382      1.001
+#>                0.013     5771      1.000
+#>                0.014     4859      1.001
+#>                0.015     4430      1.000
+#>                0.016     4135      1.001
+#>                0.042      565      1.005
+#>                0.024     1678      1.001
+#>                0.011     7736      1.000
+#>                0.014     5254      1.001
+#>                0.016     4103      1.001
+#>                0.021     2240      1.001
+#>                0.020     2527      1.001
+#>                0.026     1529      1.007
+#>                0.024     1756      1.000
+#>                0.038      692      1.001
+#>                0.024     1765      1.005
+

Finally, we can also plot the model-averaged posterior distribution +with the plot() function. We set the +prior = TRUE argument to include the prior distribution as +a grey line (and arrow for the point density at zero) and +output_scale = "r" to transform the posterior distribution +to the correlation scale (the default figure output would be on Cohen’s +d scale). (The par(mar = c(4, 4, 1, 4)) call +increases the left margin of the figure, so the secondary y-axis text is +not cut off.)

+
+par(mar = c(4, 4, 1, 4))
+plot(fit_RoBMA, prior = TRUE, output_scale = "r", )
+

+
+

Specifying Different Priors +

+

The RoBMA package allows us to fit ensembles of highly +customized meta-analytic models. Here we reproduce the ensemble for the +perinull directional hypothesis test from the Appendix (see the R +package vignettes for more examples and details). Instead of using the +fully pre-specified model with the model = "PSMA" argument, +we explicitly specify the prior distribution for models assuming +presence of the effect with the +priors_effect = prior("normal", parameters = list(mean = 0.60, sd = 0.20), truncation = list(0, Inf)) +argument, which assigns Normal(0.60, 0.20) distribution bounded to the +positive numbers to the +μ\mu +parameter (note that the prior distribution is specified on the Cohen’s +d scale, corresponding to 95% prior probability mass contained +approximately in the +ρ\rho += (0.10, 0.45) interval). Similarly, we also replace the default prior +distribution for the models assuming absence of the effect with a +perinull hypothesis with the +priors_effect_null = prior("normal", parameters = list(mean = 0, sd = 0.10)) +argument that sets 95% prior probability mass to values in the +ρ\rho += (-0.10, 0.10) interval.

+
+fit_RoBMA2 <- RoBMA(r = df$r, n = df$n, seed = 2, parallel = TRUE,
+                    priors_effect      = prior("normal", parameters = list(mean = 0.60, sd = 0.20), truncation = list(0, Inf)),
+                    priors_effect_null = prior("normal", parameters = list(mean = 0,    sd = 0.10)))
+

As previously, we can use the summary() function to +inspect the model fit and verify that the specified models correspond to +the settings.

+
+summary(fit_RoBMA2, type = "models")
+#> Call:
+#> RoBMA(r = df$r, n = df$n, priors_effect = prior("normal", parameters = list(mean = 0.6, 
+#>     sd = 0.2), truncation = list(0, Inf)), priors_effect_null = prior("normal", 
+#>     parameters = list(mean = 0, sd = 0.1)), parallel = TRUE, 
+#>     save = "min", seed = 2)
+#> 
+#> Robust Bayesian meta-analysis
+#> Models overview:
+#>  Model       Prior Effect       Prior Heterogeneity
+#>      1           Normal(0, 0.1)            Spike(0)
+#>      2           Normal(0, 0.1)            Spike(0)
+#>      3           Normal(0, 0.1)            Spike(0)
+#>      4           Normal(0, 0.1)            Spike(0)
+#>      5           Normal(0, 0.1)            Spike(0)
+#>      6           Normal(0, 0.1)            Spike(0)
+#>      7           Normal(0, 0.1)            Spike(0)
+#>      8           Normal(0, 0.1)            Spike(0)
+#>      9           Normal(0, 0.1)            Spike(0)
+#>     10           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     11           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     12           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     13           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     14           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     15           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     16           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     17           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     18           Normal(0, 0.1)   InvGamma(1, 0.15)
+#>     19 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     20 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     21 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     22 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     23 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     24 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     25 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     26 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     27 Normal(0.6, 0.2)[0, Inf]            Spike(0)
+#>     28 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     29 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     30 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     31 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     32 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     33 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     34 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     35 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>     36 Normal(0.6, 0.2)[0, Inf]   InvGamma(1, 0.15)
+#>                          Prior Bias                         Prior prob.
+#>                                                                   0.125
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)          0.010
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)       0.010
+#>                              PET ~ Cauchy(0, 1)[0, Inf]           0.031
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]           0.031
+#>                                                                   0.125
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)          0.010
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)       0.010
+#>                              PET ~ Cauchy(0, 1)[0, Inf]           0.031
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]           0.031
+#>                                                                   0.125
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)          0.010
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)       0.010
+#>                              PET ~ Cauchy(0, 1)[0, Inf]           0.031
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]           0.031
+#>                                                                   0.125
+#>            omega[two-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>        omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>            omega[one-sided: .05] ~ CumDirichlet(1, 1)             0.010
+#>      omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1)          0.010
+#>        omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)          0.010
+#>  omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1)       0.010
+#>                              PET ~ Cauchy(0, 1)[0, Inf]           0.031
+#>                            PEESE ~ Cauchy(0, 5)[0, Inf]           0.031
+#>  log(marglik) Post. prob. Inclusion BF
+#>        -18.84       0.000        0.000
+#>        -17.66       0.000        0.000
+#>        -17.06       0.000        0.000
+#>        -17.35       0.000        0.000
+#>        -17.04       0.000        0.000
+#>        -18.11       0.000        0.000
+#>        -17.69       0.000        0.000
+#>         -5.24       0.000        0.000
+#>         -7.61       0.000        0.000
+#>         -3.20       0.000        0.003
+#>         -1.45       0.000        0.022
+#>         -0.42       0.001        0.061
+#>          3.01       0.020        1.939
+#>          3.19       0.024        2.317
+#>          3.09       0.022        2.104
+#>          3.46       0.031        3.062
+#>          3.64       0.112        3.909
+#>          2.35       0.031        0.986
+#>        -11.84       0.000        0.000
+#>        -11.88       0.000        0.000
+#>        -11.71       0.000        0.000
+#>        -11.54       0.000        0.000
+#>        -11.70       0.000        0.000
+#>        -12.05       0.000        0.000
+#>        -12.07       0.000        0.000
+#>         -8.38       0.000        0.000
+#>         -7.36       0.000        0.000
+#>          3.35       0.337        3.564
+#>          3.13       0.023        2.190
+#>          3.42       0.030        2.951
+#>          4.12       0.061        6.123
+#>          3.85       0.046        4.602
+#>          3.94       0.050        5.027
+#>          3.84       0.046        4.572
+#>          3.23       0.074        2.492
+#>          3.44       0.092        3.132
+
+
+
+

References +

+
+
+Bartoš, F., & Maier, M. (2020). RoBMA: +An R package for robust Bayesian +meta-analyses. https://CRAN.R-project.org/package=RoBMA +
+
+Bartoš, F., Maier, Maximilian, Quintana, D. S., & Wagenmakers, E.-J. +(2022). Adjusting for publication bias in JASP and +R — Selection models, PET-PEESE, +and robust Bayesian meta-analysis. Advances in Methods +and Practices in Psychological Science, 5(3), 1–19. https://doi.org/10.1177/25152459221109259 +
+
+Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & +Stanley, T. D. (2023). Robust Bayesian meta-analysis: +Model-averaging across complementary publication bias +adjustment methods. Research Synthesis Methods, 14(1), +99–116. https://doi.org/10.1002/jrsm.1594 +
+
+Coburn, K. M., Vevea, J. L., & Coburn, M. K. M. (2019). weightr: Estimating weight-function +models for publication bias. +Https://CRAN.R-Project.org/Package=weightr. +
+
+Iyengar, S., & Greenhouse, J. B. (1988). Selection models and the +file drawer problem. Statistical Science, 3(1), +109–117. https://doi.org/10.1214/ss/1177013012 +
+
+Lui, P. P. (2015). Intergenerational cultural conflict, mental health, +and educational outcomes among Asian and +Latino/a Americans: Qualitative +and meta-analytic review. Psychological Bulletin, +141(2), 404–446. https://doi.org/10.1037/a0038449 +
+
+Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2023). Robust +Bayesian meta-analysis: Addressing publication +bias with model-averaging. Psychological Methods, +28(1), 107–122. https://doi.org/10.1037/met0000405 +
+
+Stanley, T. D. (2017). Limitations of PET-PEESE and other +meta-analysis methods. Social Psychological and Personality +Science, 8(5), 581–591. https://doi.org/10.1177/1948550617693062 +
+
+Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression +approximations to reduce publication selection bias. Research +Synthesis Methods, 5(1), 60–78. https://doi.org/10.1002/jrsm.1095 +
+
+Vevea, J. L., & Hedges, L. V. (1995). A general linear model for +estimating effect size in the presence of publication bias. +Psychometrika, 60(3), 419–435. https://doi.org/10.1007/BF02294384 +
+
+Wolfgang, V. (2010). Conducting meta-analyses in R with the +metafor package. Journal of Statistical +Software, 36(3), 1–48. https://www.jstatsoft.org/v36/i03/ +
+
+
+
+
+ + + +
+

Developed by František Bartoš, Maximilian Maier.

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+ +
+
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+ + +
+
+
+ +

Articles

+
+ +
+

All vignettes

+
+ +
Fitting Custom Meta-Analytic Ensembles
+
+
Hierarchical Bayesian Model-Averaged Meta-Analysis
+
+
Informed Bayesian Model-Averaged Meta-Analysis with Binary Outcomes
+
+
Informed Bayesian Model-Averaged Meta-Analysis in Medicine
+
+
Robust Bayesian Model-Averaged Meta-Regression
+
+
Reproducing Bayesian Model-Averaged Meta-Analysis
+
+
Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis
+
+
+
+ + +
+

Developed by František Bartoš, Maximilian Maier.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
+ + + + + + + diff --git a/docs/authors.html b/docs/authors.html new file mode 100644 index 00000000..b4994c60 --- /dev/null +++ b/docs/authors.html @@ -0,0 +1,109 @@ + +Authors and Citation • RoBMA + Skip to contents + + +
+
+
+ +

Authors and Citation

+
+ +
+

Authors

+ +
  • +

    František Bartoš. Author, maintainer. +

    +
    • +
  • +

    Maximilian Maier. Author. +

    +
    • +
  • +

    Eric-Jan Wagenmakers. Thesis advisor. +

    +
    • +
  • +

    Joris Goosen. Contributor. +

    +
    • +
  • +

    Matthew Denwood. Copyright holder. +
    Original copyright holder of some modified code where indicated.

    +
    • +
  • +

    Martyn Plummer. Copyright holder. +
    Original copyright holder of some modified code where indicated.

    +
    • +
+ +
+

Citation

+

Source: inst/CITATION

+ +

Bartoš F, Maier M (2020). +“RoBMA: An R Package for Robust Bayesian Meta-Analyses.” +R package version 3.2.0, https://CRAN.R-project.org/package=RoBMA. +

+ 
@Misc{,
+  title = {RoBMA: An R Package for Robust Bayesian Meta-Analyses},
+  author = {František Bartoš and Maximilian Maier},
+  year = {2020},
+  note = {R package version 3.2.0},
+  url = {https://CRAN.R-project.org/package=RoBMA},
+}
+
+ +
+ + +
+

Developed by František Bartoš, Maximilian Maier.

+
+ +
+

Site built with pkgdown 2.1.1.

+
+ +
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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=r(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(N.off(this._config.target,ms),N.on(this._config.target,ms,bs,(t=>{const 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object[0] : object\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object))\n }\n\n return null\n}\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])')\n\n if (!closedDetails) {\n return elementIsVisible\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary')\n if (summary && summary.parentNode !== closedDetails) {\n return false\n }\n\n if (summary === null) {\n return false\n }\n }\n\n return elementIsVisible\n}\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true\n }\n\n if (element.classList.contains('disabled')) {\n return true\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode()\n return root instanceof ShadowRoot ? root : null\n }\n\n if (element instanceof ShadowRoot) {\n return element\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null\n }\n\n return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery\n }\n\n return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback()\n }\n })\n }\n\n DOMContentLoadedCallbacks.push(callback)\n } else {\n callback()\n }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery()\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME\n const JQUERY_NO_CONFLICT = $.fn[name]\n $.fn[name] = plugin.jQueryInterface\n $.fn[name].Constructor = plugin\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT\n return plugin.jQueryInterface\n }\n }\n })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback)\n return\n }\n\n const durationPadding = 5\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n let called = false\n\n const handler = ({ target }) => {\n if (target !== transitionElement) {\n return\n }\n\n called = true\n transitionElement.removeEventListener(TRANSITION_END, handler)\n execute(callback)\n }\n\n transitionElement.addEventListener(TRANSITION_END, handler)\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement)\n }\n }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length\n let index = list.indexOf(activeElement)\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n }\n\n index += shouldGetNext ? 1 : -1\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n defineJQueryPlugin,\n execute,\n executeAfterTransition,\n findShadowRoot,\n getElement,\n getjQuery,\n getNextActiveElement,\n getTransitionDurationFromElement,\n getUID,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop,\n onDOMContentLoaded,\n parseSelector,\n reflow,\n triggerTransitionEnd,\n toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n 'click',\n 'dblclick',\n 'mouseup',\n 'mousedown',\n 'contextmenu',\n 'mousewheel',\n 'DOMMouseScroll',\n 'mouseover',\n 'mouseout',\n 'mousemove',\n 'selectstart',\n 'selectend',\n 'keydown',\n 'keypress',\n 'keyup',\n 'orientationchange',\n 'touchstart',\n 'touchmove',\n 'touchend',\n 'touchcancel',\n 'pointerdown',\n 'pointermove',\n 'pointerup',\n 'pointerleave',\n 'pointercancel',\n 'gesturestart',\n 'gesturechange',\n 'gestureend',\n 'focus',\n 'blur',\n 'change',\n 'reset',\n 'select',\n 'submit',\n 'focusin',\n 'focusout',\n 'load',\n 'unload',\n 'beforeunload',\n 'resize',\n 'move',\n 'DOMContentLoaded',\n 'readystatechange',\n 'error',\n 'abort',\n 'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element)\n\n element.uidEvent = uid\n eventRegistry[uid] = eventRegistry[uid] || {}\n\n return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, { delegateTarget: element })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn)\n }\n\n return fn.apply(element, [event])\n }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector)\n\n for (let { target } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue\n }\n\n hydrateObj(event, { delegateTarget: target })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn)\n }\n\n return fn.apply(target, [event])\n }\n }\n }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events)\n .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n let typeEvent = getTypeEvent(originalTypeEvent)\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent\n }\n\n return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n return fn.call(this, event)\n }\n }\n }\n\n callable = wrapFunction(callable)\n }\n\n const events = getElementEvents(element)\n const handlers = events[typeEvent] || (events[typeEvent] = {})\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n return\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n const fn = isDelegated ?\n bootstrapDelegationHandler(element, handler, callable) :\n bootstrapHandler(element, callable)\n\n fn.delegationSelector = isDelegated ? handler : null\n fn.callable = callable\n fn.oneOff = oneOff\n fn.uidEvent = uid\n handlers[uid] = fn\n\n element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n if (!fn) {\n return\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {}\n\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '')\n return customEvents[event] || event\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false)\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true)\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n const inNamespace = typeEvent !== originalTypeEvent\n const events = getElementEvents(element)\n const storeElementEvent = events[typeEvent] || {}\n const isNamespace = originalTypeEvent.startsWith('.')\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n return\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n }\n }\n\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null\n }\n\n const $ = getjQuery()\n const typeEvent = getTypeEvent(event)\n const inNamespace = event !== typeEvent\n\n let jQueryEvent = null\n let bubbles = true\n let nativeDispatch = true\n let defaultPrevented = false\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args)\n\n $(element).trigger(jQueryEvent)\n bubbles = !jQueryEvent.isPropagationStopped()\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n defaultPrevented = jQueryEvent.isDefaultPrevented()\n }\n\n const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n if (defaultPrevented) {\n evt.preventDefault()\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt)\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault()\n }\n\n return evt\n }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value\n } catch {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value\n }\n })\n }\n }\n\n return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true\n }\n\n if (value === 'false') {\n return false\n }\n\n if (value === Number(value).toString()) {\n return Number(value)\n }\n\n if (value === '' || value === 'null') {\n return null\n }\n\n if (typeof value !== 'string') {\n return value\n }\n\n try {\n return JSON.parse(decodeURIComponent(value))\n } catch {\n return value\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {}\n }\n\n const attributes = {}\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '')\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n attributes[pureKey] = normalizeData(element.dataset[key])\n }\n\n return attributes\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {}\n }\n\n static get DefaultType() {\n return {}\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!')\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n return config\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n }\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property]\n const valueType = isElement(value) ? 'element' : toType(value)\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(\n `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n )\n }\n }\n }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super()\n\n element = getElement(element)\n if (!element) {\n return\n }\n\n this._element = element\n this._config = this._getConfig(config)\n\n Data.set(this._element, this.constructor.DATA_KEY, this)\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY)\n EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated)\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY)\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n }\n\n static get VERSION() {\n return VERSION\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`\n }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target')\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href')\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n return null\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n }\n\n return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector)\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector))\n },\n\n parents(element, selector) {\n const parents = []\n let ancestor = element.parentNode.closest(selector)\n\n while (ancestor) {\n parents.push(ancestor)\n ancestor = ancestor.parentNode.closest(selector)\n }\n\n return parents\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous]\n }\n\n previous = previous.previousElementSibling\n }\n\n return []\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling\n\n while (next) {\n if (next.matches(selector)) {\n return [next]\n }\n\n next = next.nextElementSibling\n }\n\n return []\n },\n\n focusableChildren(element) {\n const focusables = [\n 'a',\n 'button',\n 'input',\n 'textarea',\n 'select',\n 'details',\n '[tabindex]',\n '[contenteditable=\"true\"]'\n ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n },\n\n getSelectorFromElement(element) {\n const selector = getSelector(element)\n\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null\n }\n\n return null\n },\n\n getElementFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.findOne(selector) : null\n },\n\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.find(selector) : []\n }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`\n const name = component.NAME\n\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n const instance = component.getOrCreateInstance(target)\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]()\n })\n}\n\nexport {\n enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n if (closeEvent.defaultPrevented) {\n return\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n }\n\n // Private\n _destroyElement() {\n this._element.remove()\n EventHandler.trigger(this._element, EVENT_CLOSED)\n this.dispose()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this)\n\n if (config === 'toggle') {\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n event.preventDefault()\n\n const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n const data = Button.getOrCreateInstance(button)\n\n data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n}\n\nconst DefaultType = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super()\n this._element = element\n\n if (!element || !Swipe.isSupported()) {\n return\n }\n\n this._config = this._getConfig(config)\n this._deltaX = 0\n this._supportPointerEvents = Boolean(window.PointerEvent)\n this._initEvents()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY)\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX\n\n return\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX\n }\n\n this._handleSwipe()\n execute(this._config.endCallback)\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ?\n 0 :\n event.touches[0].clientX - this._deltaX\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX)\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return\n }\n\n const direction = absDeltaX / this._deltaX\n\n this._deltaX = 0\n\n if (!direction) {\n return\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getNextActiveElement,\n isRTL,\n isVisible,\n reflow,\n triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n}\n\nconst DefaultType = {\n interval: '(number|boolean)', // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._interval = null\n this._activeElement = null\n this._isSliding = false\n this.touchTimeout = null\n this._swipeHelper = null\n\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n this._addEventListeners()\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT)\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next()\n }\n }\n\n prev() {\n this._slide(ORDER_PREV)\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element)\n }\n\n this._clearInterval()\n }\n\n cycle() {\n this._clearInterval()\n this._updateInterval()\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n return\n }\n\n this.cycle()\n }\n\n to(index) {\n const items = this._getItems()\n if (index > items.length - 1 || index < 0) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n return\n }\n\n const activeIndex = this._getItemIndex(this._getActive())\n if (activeIndex === index) {\n return\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n this._slide(order, items[index])\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose()\n }\n\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval\n return config\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners()\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause()\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout)\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n }\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n }\n\n this._swipeHelper = new Swipe(this._element, swipeConfig)\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return\n }\n\n const direction = KEY_TO_DIRECTION[event.key]\n if (direction) {\n event.preventDefault()\n this._slide(this._directionToOrder(direction))\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element)\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n activeIndicator.removeAttribute('aria-current')\n\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n newActiveIndicator.setAttribute('aria-current', 'true')\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive()\n\n if (!element) {\n return\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n this._config.interval = elementInterval || this._config.defaultInterval\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return\n }\n\n const activeElement = this._getActive()\n const isNext = order === ORDER_NEXT\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n if (nextElement === activeElement) {\n return\n }\n\n const nextElementIndex = this._getItemIndex(nextElement)\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n })\n }\n\n const slideEvent = triggerEvent(EVENT_SLIDE)\n\n if (slideEvent.defaultPrevented) {\n return\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return\n }\n\n const isCycling = Boolean(this._interval)\n this.pause()\n\n this._isSliding = true\n\n this._setActiveIndicatorElement(nextElementIndex)\n this._activeElement = nextElement\n\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n nextElement.classList.add(orderClassName)\n\n reflow(nextElement)\n\n activeElement.classList.add(directionalClassName)\n nextElement.classList.add(directionalClassName)\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n this._isSliding = false\n\n triggerEvent(EVENT_SLID)\n }\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n if (isCycling) {\n this.cycle()\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE)\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element)\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval)\n this._interval = null\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config)\n\n if (typeof config === 'number') {\n data.to(config)\n return\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return\n }\n\n event.preventDefault()\n\n const carousel = Carousel.getOrCreateInstance(target)\n const slideIndex = this.getAttribute('data-bs-slide-to')\n\n if (slideIndex) {\n carousel.to(slideIndex)\n carousel._maybeEnableCycle()\n return\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next()\n carousel._maybeEnableCycle()\n return\n }\n\n carousel.prev()\n carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel)\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getElement,\n reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n parent: null,\n toggle: true\n}\n\nconst DefaultType = {\n parent: '(null|element)',\n toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isTransitioning = false\n this._triggerArray = []\n\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem)\n const filterElement = SelectorEngine.find(selector)\n .filter(foundElement => foundElement === this._element)\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem)\n }\n }\n\n this._initializeChildren()\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n }\n\n if (this._config.toggle) {\n this.toggle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide()\n } else {\n this.show()\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return\n }\n\n let activeChildren = []\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n .filter(element => element !== this._element)\n .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n if (startEvent.defaultPrevented) {\n return\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide()\n }\n\n const dimension = this._getDimension()\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE)\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n this._element.style[dimension] = 0\n\n this._addAriaAndCollapsedClass(this._triggerArray, true)\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n this._element.style[dimension] = ''\n\n EventHandler.trigger(this._element, EVENT_SHOWN)\n }\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n const scrollSize = `scroll${capitalizedDimension}`\n\n this._queueCallback(complete, this._element, true)\n this._element.style[dimension] = `${this._element[scrollSize]}px`\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n if (startEvent.defaultPrevented) {\n return\n }\n\n const dimension = this._getDimension()\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger)\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false)\n }\n }\n\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE)\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._element.style[dimension] = ''\n\n this._queueCallback(complete, this._element, true)\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle) // Coerce string values\n config.parent = getElement(config.parent)\n return config\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element)\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected))\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n element.setAttribute('aria-expanded', isOpen)\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {}\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n event.preventDefault()\n }\n\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n execute,\n getElement,\n getNextActiveElement,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n}\n\nconst DefaultType = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._popper = null\n this._parent = this._element.parentNode // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n this._inNavbar = this._detectNavbar()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show()\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._createPopper()\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n this._element.focus()\n this._element.setAttribute('aria-expanded', true)\n\n this._menu.classList.add(CLASS_NAME_SHOW)\n this._element.classList.add(CLASS_NAME_SHOW)\n EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n this._completeHide(relatedTarget)\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n update() {\n this._inNavbar = this._detectNavbar()\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._element.setAttribute('aria-expanded', 'false')\n Manipulator.removeDataAttribute(this._menu, 'popper')\n EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n }\n\n _getConfig(config) {\n config = super._getConfig(config)\n\n if (typeof config.reference === 'object' && !isElement(config.reference) &&\n typeof config.reference.getBoundingClientRect !== 'function'\n ) {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n }\n\n return config\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n }\n\n let referenceElement = this._element\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference)\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference\n }\n\n const popperConfig = this._getPopperConfig()\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW)\n }\n\n _getPlacement() {\n const parentDropdown = this._parent\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _selectMenuItem({ key, target }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n if (!items.length) {\n return\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n return\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle)\n if (!context || context._config.autoClose === false) {\n continue\n }\n\n const composedPath = event.composedPath()\n const isMenuTarget = composedPath.includes(context._menu)\n if (\n composedPath.includes(context._element) ||\n (context._config.autoClose === 'inside' && !isMenuTarget) ||\n (context._config.autoClose === 'outside' && isMenuTarget)\n ) {\n continue\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue\n }\n\n const relatedTarget = { relatedTarget: context._element }\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event\n }\n\n context._completeHide(relatedTarget)\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName)\n const isEscapeEvent = event.key === ESCAPE_KEY\n const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return\n }\n\n if (isInput && !isEscapeEvent) {\n return\n }\n\n event.preventDefault()\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n this :\n (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n if (isUpOrDownEvent) {\n event.stopPropagation()\n instance.show()\n instance._selectMenuItem(event)\n return\n }\n\n if (instance._isShown()) { // else is escape and we check if it is shown\n event.stopPropagation()\n instance.hide()\n getToggleButton.focus()\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n event.preventDefault()\n Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isAppended = false\n this._element = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._append()\n\n const element = this._getElement()\n if (this._config.isAnimated) {\n reflow(element)\n }\n\n element.classList.add(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n execute(callback)\n })\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n this.dispose()\n execute(callback)\n })\n }\n\n dispose() {\n if (!this._isAppended) {\n return\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n this._element.remove()\n this._isAppended = false\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div')\n backdrop.className = this._config.className\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE)\n }\n\n this._element = backdrop\n }\n\n return this._element\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement)\n return config\n }\n\n _append() {\n if (this._isAppended) {\n return\n }\n\n const element = this._getElement()\n this._config.rootElement.append(element)\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback)\n })\n\n this._isAppended = true\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n autofocus: 'boolean',\n trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isActive = false\n this._lastTabNavDirection = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus()\n }\n\n EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n this._isActive = true\n }\n\n deactivate() {\n if (!this._isActive) {\n return\n }\n\n this._isActive = false\n EventHandler.off(document, EVENT_KEY)\n }\n\n // Private\n _handleFocusin(event) {\n const { trapElement } = this._config\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement)\n\n if (elements.length === 0) {\n trapElement.focus()\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus()\n } else {\n elements[0].focus()\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth\n return Math.abs(window.innerWidth - documentWidth)\n }\n\n hide() {\n const width = this.getWidth()\n this._disableOverFlow()\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow')\n this._resetElementAttributes(this._element, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n }\n\n isOverflowing() {\n return this.getWidth() > 0\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow')\n this._element.style.overflow = 'hidden'\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth()\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return\n }\n\n this._saveInitialAttribute(element, styleProperty)\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty)\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue)\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty)\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty)\n return\n }\n\n Manipulator.removeDataAttribute(element, styleProperty)\n element.style.setProperty(styleProperty, value)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector)\n return\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel)\n }\n }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n backdrop: true,\n focus: true,\n keyboard: true\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._isShown = false\n this._isTransitioning = false\n this._scrollBar = new ScrollBarHelper()\n\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n relatedTarget\n })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._isTransitioning = true\n\n this._scrollBar.hide()\n\n document.body.classList.add(CLASS_NAME_OPEN)\n\n this._adjustDialog()\n\n this._backdrop.show(() => this._showElement(relatedTarget))\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._isShown = false\n this._isTransitioning = true\n this._focustrap.deactivate()\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n }\n\n dispose() {\n EventHandler.off(window, EVENT_KEY)\n EventHandler.off(this._dialog, EVENT_KEY)\n\n this._backdrop.dispose()\n this._focustrap.deactivate()\n\n super.dispose()\n }\n\n handleUpdate() {\n this._adjustDialog()\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element)\n }\n\n this._element.style.display = 'block'\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.scrollTop = 0\n\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n if (modalBody) {\n modalBody.scrollTop = 0\n }\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate()\n }\n\n this._isTransitioning = false\n EventHandler.trigger(this._element, EVENT_SHOWN, {\n relatedTarget\n })\n }\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n this._triggerBackdropTransition()\n })\n\n EventHandler.on(window, EVENT_RESIZE, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog()\n }\n })\n\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition()\n return\n }\n\n if (this._config.backdrop) {\n this.hide()\n }\n })\n })\n }\n\n _hideModal() {\n this._element.style.display = 'none'\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._isTransitioning = false\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN)\n this._resetAdjustments()\n this._scrollBar.reset()\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n })\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE)\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const initialOverflowY = this._element.style.overflowY\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden'\n }\n\n this._element.classList.add(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY\n }, this._dialog)\n }, this._dialog)\n\n this._element.focus()\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const scrollbarWidth = this._scrollBar.getWidth()\n const isBodyOverflowing = scrollbarWidth > 0\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = ''\n this._element.style.paddingRight = ''\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](relatedTarget)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n EventHandler.one(target, EVENT_SHOW, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n if (isVisible(this)) {\n this.focus()\n }\n })\n })\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide()\n }\n\n const data = Modal.getOrCreateInstance(target)\n\n data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n defineJQueryPlugin,\n isDisabled,\n isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n scroll: false\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isShown = false\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._backdrop.show()\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide()\n }\n\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.classList.add(CLASS_NAME_SHOWING)\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate()\n }\n\n this._element.classList.add(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOWING)\n EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n }\n\n this._queueCallback(completeCallBack, this._element, true)\n }\n\n hide() {\n if (!this._isShown) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._focustrap.deactivate()\n this._element.blur()\n this._isShown = false\n this._element.classList.add(CLASS_NAME_HIDING)\n this._backdrop.hide()\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset()\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._queueCallback(completeCallback, this._element, true)\n }\n\n dispose() {\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n return\n }\n\n this.hide()\n }\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop)\n\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n })\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus()\n }\n })\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide()\n }\n\n const data = Offcanvas.getOrCreateInstance(target)\n data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show()\n }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide()\n }\n }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n 'background',\n 'cite',\n 'href',\n 'itemtype',\n 'longdesc',\n 'poster',\n 'src',\n 'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase()\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n }\n\n return true\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml)\n }\n\n const domParser = new window.DOMParser()\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase()\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove()\n continue\n }\n\n const attributeList = [].concat(...element.attributes)\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName)\n }\n }\n }\n\n return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n allowList: DefaultAllowlist,\n content: {}, // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n}\n\nconst DefaultType = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n}\n\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content)\n .map(config => this._resolvePossibleFunction(config))\n .filter(Boolean)\n }\n\n hasContent() {\n return this.getContent().length > 0\n }\n\n changeContent(content) {\n this._checkContent(content)\n this._config.content = { ...this._config.content, ...content }\n return this\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div')\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector)\n }\n\n const template = templateWrapper.children[0]\n const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '))\n }\n\n return template\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config)\n this._checkContent(config.content)\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template)\n\n if (!templateElement) {\n return\n }\n\n content = this._resolvePossibleFunction(content)\n\n if (!content) {\n templateElement.remove()\n return\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement)\n return\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content)\n return\n }\n\n templateElement.textContent = content\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this])\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = ''\n templateElement.append(element)\n return\n }\n\n templateElement.textContent = element.textContent\n }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' +\n '
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',\n title: '',\n trigger: 'hover focus'\n}\n\nconst DefaultType = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n }\n\n super(element, config)\n\n // Private\n this._isEnabled = true\n this._timeout = 0\n this._isHovered = null\n this._activeTrigger = {}\n this._popper = null\n this._templateFactory = null\n this._newContent = null\n\n // Protected\n this.tip = null\n\n this._setListeners()\n\n if (!this._config.selector) {\n this._fixTitle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n enable() {\n this._isEnabled = true\n }\n\n disable() {\n this._isEnabled = false\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled\n }\n\n toggle() {\n if (!this._isEnabled) {\n return\n }\n\n this._activeTrigger.click = !this._activeTrigger.click\n if (this._isShown()) {\n this._leave()\n return\n }\n\n this._enter()\n }\n\n dispose() {\n clearTimeout(this._timeout)\n\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n }\n\n this._disposePopper()\n super.dispose()\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements')\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n const shadowRoot = findShadowRoot(this._element)\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper()\n\n const tip = this._getTipElement()\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n const { container } = this._config\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip)\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n }\n\n this._popper = this._createPopper(tip)\n\n tip.classList.add(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n if (this._isHovered === false) {\n this._leave()\n }\n\n this._isHovered = false\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n hide() {\n if (!this._isShown()) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const tip = this._getTipElement()\n tip.classList.remove(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false\n this._activeTrigger[TRIGGER_FOCUS] = false\n this._activeTrigger[TRIGGER_HOVER] = false\n this._isHovered = null // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n if (!this._isHovered) {\n this._disposePopper()\n }\n\n this._element.removeAttribute('aria-describedby')\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n update() {\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle())\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n }\n\n return this.tip\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml()\n\n // TODO: remove this check in v6\n if (!tip) {\n return null\n }\n\n tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n const tipId = getUID(this.constructor.NAME).toString()\n\n tip.setAttribute('id', tipId)\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE)\n }\n\n return tip\n }\n\n setContent(content) {\n this._newContent = content\n if (this._isShown()) {\n this._disposePopper()\n this.show()\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content)\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n })\n }\n\n return this._templateFactory\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n }\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n }\n\n _isAnimated() {\n return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n }\n\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element])\n const attachment = AttachmentMap[placement.toUpperCase()]\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element])\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [\n {\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n },\n {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n },\n {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n }\n }\n ]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ')\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context.toggle()\n })\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSEENTER) :\n this.constructor.eventName(EVENT_FOCUSIN)\n const eventOut = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSELEAVE) :\n this.constructor.eventName(EVENT_FOCUSOUT)\n\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n context._enter()\n })\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n context._element.contains(event.relatedTarget)\n\n context._leave()\n })\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide()\n }\n }\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title')\n\n if (!title) {\n return\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title)\n }\n\n this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title')\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true\n return\n }\n\n this._isHovered = true\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show()\n }\n }, this._config.delay.show)\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n this._isHovered = false\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide()\n }\n }, this._config.delay.hide)\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout)\n this._timeout = setTimeout(handler, timeout)\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true)\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute]\n }\n }\n\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n }\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container)\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n }\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString()\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString()\n }\n\n return config\n }\n\n _getDelegateConfig() {\n const config = {}\n\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value\n }\n }\n\n config.selector = false\n config.trigger = 'manual'\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy()\n this._popper = null\n }\n\n if (this.tip) {\n this.tip.remove()\n this.tip = null\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
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' +\n '
' +\n '
',\n trigger: 'click'\n}\n\nconst DefaultType = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent()\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n }\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content)\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map()\n this._observableSections = new Map()\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n this._activeTarget = null\n this._observer = null\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n }\n this.refresh() // initialize\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables()\n this._maybeEnableSmoothScroll()\n\n if (this._observer) {\n this._observer.disconnect()\n } else {\n this._observer = this._getNewObserver()\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section)\n }\n }\n\n dispose() {\n this._observer.disconnect()\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n }\n\n return config\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK)\n\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash)\n if (observableSection) {\n event.preventDefault()\n const root = this._rootElement || window\n const height = observableSection.offsetTop - this._element.offsetTop\n if (root.scrollTo) {\n root.scrollTo({ top: height, behavior: 'smooth' })\n return\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height\n }\n })\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n }\n\n return new IntersectionObserver(entries => this._observerCallback(entries), options)\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n this._process(targetElement(entry))\n }\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n this._previousScrollData.parentScrollTop = parentScrollTop\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null\n this._clearActiveClass(targetElement(entry))\n\n continue\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry)\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return\n }\n\n continue\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry)\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map()\n this._observableSections = new Map()\n\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue\n }\n\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor)\n this._observableSections.set(anchor.hash, observableSection)\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return\n }\n\n this._clearActiveClass(this._config.target)\n this._activeTarget = target\n target.classList.add(CLASS_NAME_ACTIVE)\n this._activateParents(target)\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n .classList.add(CLASS_NAME_ACTIVE)\n return\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and
')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('');n.attr("href","#"+e),n.text(t);var r=a("
  • ");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1 + + + + + + + + + + + + diff --git a/docs/deps/font-awesome-6.5.2/css/all.css b/docs/deps/font-awesome-6.5.2/css/all.css new file mode 100644 index 00000000..151dd57c --- /dev/null +++ b/docs/deps/font-awesome-6.5.2/css/all.css @@ -0,0 +1,8028 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, 0)); + transform: rotate(var(--fa-rotate-angle, 0)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-film::before { + content: "\f008"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-circle-exclamation::before { + content: "\f06a"; } + +.fa-exclamation-circle::before { + content: "\f06a"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-circle-chevron-down::before { + content: "\f13a"; } + +.fa-chevron-circle-down::before { + content: "\f13a"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-sitemap::before { + content: "\f0e8"; } + +.fa-circle-dollar-to-slot::before { + content: "\f4b9"; } + +.fa-donate::before { + content: "\f4b9"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-comment-slash::before { + content: "\f4b3"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-square-check::before { + content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-gamepad::before { + content: "\f11b"; } + +.fa-circle-dot::before { + content: "\f192"; } + +.fa-dot-circle::before { + content: "\f192"; } + +.fa-face-dizzy::before { + content: "\f567"; } + +.fa-dizzy::before { + content: "\f567"; } + +.fa-egg::before { + content: "\f7fb"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-futbol::before { + content: "\f1e3"; } + +.fa-futbol-ball::before { + content: "\f1e3"; } + +.fa-soccer-ball::before { + content: "\f1e3"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-lock::before { + content: "\f023"; } + +.fa-gas-pump::before { + content: "\f52f"; } + +.fa-hot-tub-person::before { + content: "\f593"; } + +.fa-hot-tub::before { + content: "\f593"; } + +.fa-map-location::before { + content: "\f59f"; } + +.fa-map-marked::before { + content: "\f59f"; } + +.fa-house-flood-water::before { + content: "\e50e"; } + +.fa-tree::before { + content: "\f1bb"; } + +.fa-bridge-lock::before { + content: "\e4cc"; } + +.fa-sack-dollar::before { + content: "\f81d"; } + +.fa-pen-to-square::before { + content: "\f044"; } + +.fa-edit::before { + content: "\f044"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-share-nodes::before { + content: "\f1e0"; } + +.fa-share-alt::before { + content: "\f1e0"; } + +.fa-heart-circle-minus::before { + content: "\e4ff"; } + +.fa-hourglass-half::before { + content: "\f252"; } + +.fa-hourglass-2::before { + content: "\f252"; } + +.fa-microscope::before { + content: "\f610"; } + +.fa-sink::before { + content: "\e06d"; } + +.fa-bag-shopping::before { + content: "\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-mitten::before { + content: "\f7b5"; } + 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content: "\f132"; } + +.fa-arrow-up-short-wide::before { + content: "\f885"; } + +.fa-sort-amount-up-alt::before { + content: "\f885"; } + +.fa-house-medical::before { + content: "\e3b2"; } + +.fa-golf-ball-tee::before { + content: "\f450"; } + +.fa-golf-ball::before { + content: "\f450"; } + +.fa-circle-chevron-left::before { + content: "\f137"; } + +.fa-chevron-circle-left::before { + content: "\f137"; } + +.fa-house-chimney-window::before { + content: "\e00d"; } + +.fa-pen-nib::before { + content: "\f5ad"; } + +.fa-tent-arrow-turn-left::before { + content: "\e580"; } + +.fa-tents::before { + content: "\e582"; } + +.fa-wand-magic::before { + content: "\f0d0"; } + +.fa-magic::before { + content: "\f0d0"; } + +.fa-dog::before { + content: "\f6d3"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-moon::before { + content: "\f186"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-cheese::before { + content: "\f7ef"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-music::before { + content: "\f001"; } + +.fa-code-commit::before { + content: "\f386"; } + +.fa-temperature-low::before { + content: "\f76b"; } + +.fa-person-biking::before { + content: "\f84a"; } + +.fa-biking::before { + content: "\f84a"; } + +.fa-broom::before { + content: "\f51a"; } + +.fa-shield-heart::before { + content: "\e574"; } + +.fa-gopuram::before { + content: "\f664"; } + +.fa-earth-oceania::before { + content: "\e47b"; } + +.fa-globe-oceania::before { + content: "\e47b"; } + +.fa-square-xmark::before { + content: "\f2d3"; } + +.fa-times-square::before { + content: "\f2d3"; } + +.fa-xmark-square::before { + content: "\f2d3"; } + +.fa-hashtag::before { + content: "\23"; } + +.fa-up-right-and-down-left-from-center::before { + content: "\f424"; } + +.fa-expand-alt::before { + content: "\f424"; } + +.fa-oil-can::before { + content: "\f613"; } + +.fa-t::before { + content: "\54"; } + +.fa-hippo::before { + content: "\f6ed"; } + +.fa-chart-column::before { + content: "\e0e3"; } + +.fa-infinity::before { + content: "\f534"; } + +.fa-vial-circle-check::before { + content: "\e596"; } + +.fa-person-arrow-down-to-line::before { + content: "\e538"; } + +.fa-voicemail::before { + content: "\f897"; } + +.fa-fan::before { + content: "\f863"; } + +.fa-person-walking-luggage::before { + content: "\e554"; } + +.fa-up-down::before { + content: "\f338"; } + +.fa-arrows-alt-v::before { + content: "\f338"; } + +.fa-cloud-moon-rain::before { + content: "\f73c"; } + +.fa-calendar::before { + content: "\f133"; } + +.fa-trailer::before { + content: "\e041"; } + +.fa-bahai::before { + content: "\f666"; } + +.fa-haykal::before { + content: "\f666"; } + +.fa-sd-card::before { + content: "\f7c2"; } + +.fa-dragon::before { + content: "\f6d5"; } + +.fa-shoe-prints::before { + content: "\f54b"; } + +.fa-circle-plus::before { + content: "\f055"; } + +.fa-plus-circle::before { + content: "\f055"; } + 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} + +.fa-border-all::before { + content: "\f84c"; } + +.fa-face-angry::before { + content: "\f556"; } + +.fa-angry::before { + content: "\f556"; } + +.fa-cookie-bite::before { + content: "\f564"; } + +.fa-arrow-trend-down::before { + content: "\e097"; } + +.fa-rss::before { + content: "\f09e"; } + +.fa-feed::before { + content: "\f09e"; } + +.fa-draw-polygon::before { + content: "\f5ee"; } + +.fa-scale-balanced::before { + content: "\f24e"; } + +.fa-balance-scale::before { + content: "\f24e"; } + +.fa-gauge-simple-high::before { + content: "\f62a"; } + +.fa-tachometer::before { + content: "\f62a"; } + +.fa-tachometer-fast::before { + content: "\f62a"; } + +.fa-shower::before { + content: "\f2cc"; } + +.fa-desktop::before { + content: "\f390"; } + +.fa-desktop-alt::before { + content: "\f390"; } + +.fa-m::before { + content: "\4d"; } + +.fa-table-list::before { + content: "\f00b"; } + +.fa-th-list::before { + content: "\f00b"; } + +.fa-comment-sms::before { + content: "\f7cd"; } + 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+ content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-dungeon::before { + content: "\f6d9"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-money-bill-1-wave::before { + content: "\f53b"; } + +.fa-money-bill-wave-alt::before { + content: "\f53b"; } + +.fa-life-ring::before { + content: "\f1cd"; } + +.fa-hands::before { + content: "\f2a7"; } + +.fa-sign-language::before { + content: "\f2a7"; } + +.fa-signing::before { + content: "\f2a7"; } + +.fa-calendar-day::before { + content: "\f783"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-arrows-up-down::before { + content: "\f07d"; } + +.fa-arrows-v::before { + content: "\f07d"; } + +.fa-face-grimace::before { + content: "\f57f"; } + +.fa-grimace::before { + content: "\f57f"; } + +.fa-wheelchair-move::before { + content: "\e2ce"; } + +.fa-wheelchair-alt::before { + content: "\e2ce"; } + +.fa-turn-down::before { + content: "\f3be"; } + +.fa-level-down-alt::before { + content: "\f3be"; } + +.fa-person-walking-arrow-right::before { + content: "\e552"; } + +.fa-square-envelope::before { + content: "\f199"; } + +.fa-envelope-square::before { + content: "\f199"; } + +.fa-dice::before { + content: "\f522"; } + +.fa-bowling-ball::before { + content: "\f436"; } + +.fa-brain::before { + content: "\f5dc"; } + +.fa-bandage::before { + content: "\f462"; } + +.fa-band-aid::before { + content: "\f462"; } + +.fa-calendar-minus::before { + content: "\f272"; } + +.fa-circle-xmark::before { + content: "\f057"; } + +.fa-times-circle::before { + content: "\f057"; } + +.fa-xmark-circle::before { + content: "\f057"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-earth-asia::before { + content: "\f57e"; } + +.fa-globe-asia::before { + content: "\f57e"; } + +.fa-id-card-clip::before { + content: "\f47f"; } + +.fa-id-card-alt::before { + content: "\f47f"; } + +.fa-magnifying-glass-plus::before { + content: "\f00e"; } + +.fa-search-plus::before { + content: "\f00e"; } + +.fa-thumbs-up::before { + content: "\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-pixiv:before { + content: "\e640"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + +.fa-js-square:before { + content: "\f3b9"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-jxl:before { + content: "\e67b"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-brave:before { + content: "\e63c"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: "\e055"; } + +.fa-instagram-square:before { + content: "\e055"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-square-hacker-news:before { + content: "\f3af"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + 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Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + 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} + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; 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} + +.fa.fa-themeisle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official:before { + content: "\f2b3"; } + +.fa.fa-google-plus-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-circle:before { + content: "\f2b3"; } + +.fa.fa-font-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa:before { + content: "\f2b4"; } + +.fa.fa-handshake-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-handshake-o:before { + content: "\f2b5"; } + +.fa.fa-envelope-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-envelope-open-o:before { + content: "\f2b6"; } + +.fa.fa-linode { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-address-book-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-book-o:before { + content: "\f2b9"; } + +.fa.fa-vcard:before { + content: "\f2bb"; } + +.fa.fa-address-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-card-o:before { + content: "\f2bb"; } + +.fa.fa-vcard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-vcard-o:before { + content: "\f2bb"; } + +.fa.fa-user-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-circle-o:before { + content: "\f2bd"; } + +.fa.fa-user-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-o:before { + content: "\f007"; } + +.fa.fa-id-badge { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-drivers-license:before { + content: "\f2c2"; } + +.fa.fa-id-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-id-card-o:before { + content: "\f2c2"; } + +.fa.fa-drivers-license-o 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content: "\f2dc"; } + +.fa.fa-superpowers { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpexplorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-meetup { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } diff --git a/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css new file mode 100644 index 00000000..09baf5fc --- /dev/null +++ b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css @@ -0,0 +1,6 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 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Hide your header until you need it + * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery); \ No newline at end of file diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.js new file mode 100644 index 00000000..fc6c299b --- /dev/null +++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). 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[ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
    " ], + col: [ 2, "", "
    " ], + tr: [ 2, "", "
    " ], + td: [ 3, "", "
    " ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? 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    +
    +
    +

    README +

    + + + +
    +
    +

    Robust Bayesian Meta-Analysis (RoBMA) +

    +

    This package estimates an ensemble of meta-analytic models (assuming either the presence or absence of effect, heterogeneity, and publication bias) and uses Bayesian model averaging to combine them. The ensemble uses Bayes factors to test for the presence of absence of the individual components (e.g., effect vs. no effect) and model-averages parameter estimates based on posterior model probabilities. The user can define a wide range prior distributions for the effect size, heterogeneity, and publication bias components (including selection, PET, and PEESE style models). The package provides convenient functions for summary, visualizations, and fit diagnostics.

    +

    See our manuscripts that for technical details and examples:

    +
      +
    • Bartoš, Maier, Stanley, et al. (2023) (https://doi.org/10.31234/osf.io/98xb5) extends RoBMA-PSMA into meta-regression
      • +
    • Bartoš, Otte, et al. (2023) (https://doi.org/10.48550/arXiv.2306.11468) outlines binomial-normal Bayesian model-averaged meta-analysis for binary outcomes (+ develops informed prior distributions for log OR, log RR, RD, and log HR in medical settings, also see Bartoš et al. (2021) for informed prior distributions for Cohen’s d, based on the Cochrane Database of Systematic Reviews)
      • +
    • Bartoš, Maier, Wagenmakers, et al. (2023) (https://doi.org/10.1002/jrsm.1594) describes the newest version of publication bias adjustment, RoBMA-PSMA, which combines selection models and PET-PEESE,
      • +
    • Maier et al. (2023) (https://doi.org/10.1037/met0000405) introduces the RoBMA framework and the original version of the method,
      • +
    • Bartoš et al. (2022) (https://doi.org/10.1177/25152459221109259) provides an accessible tutorial on the method including the implementation in the the user-friendly graphical user interface of JASP (JASP Team, 2020)
      • +
    +

    We also prepared multiple vignettes that illustrate functionality of the package:

    + +
    +

    Updates +

    +
    +

    Backwards Compatibility +

    +

    Please note that the major releases of RoBMA break backwards compatibility. The latest version of RoBMA 1 can be installed using

    +
    +remotes::install_version("RoBMA", version = "1.2.1")
    +

    and the latest version of RoBMA 2 can be installed using

    +
    +remotes::install_version("RoBMA", version = "2.3.2")
    +

    (Or use the source packages archived with at OSF repositories associated with the corresponding projects.)

    +
    +
    +

    News +

    +

    The 3.0 version brings several features to the package:

    + +

    The 2.0 version brought several updates to the package:

    +
      +
    • naming of the arguments specifying prior distributions for the different parameters/components of the models changed (priors_mu -> priors_effect, priors_tau -> priors_heterogeneity, and priors_omega -> priors_bias),
      • +
    • prior distributions for specifying weight functions now use a dedicated function (prior(distribution = "two.sided", parameters = ...) -> prior_weightfunction(distribution = "two.sided", parameters = ...)),
      • +
    • new dedicated function for specifying no publication bias adjustment component / no heterogeneity component (prior_none()),
      • +
    • new dedicated functions for specifying models with the PET and PEESE publication bias adjustments (prior_PET(distribution = "Cauchy", parameters = ...) and prior_PEESE(distribution = "Cauchy", parameters = ...)),
      • +
    • new default prior distribution specification for the publication bias adjustment part of the models (corresponding to the RoBMA-PSMA model from Bartoš, Maier, Wagenmakers, et al. (2023)),
      • +
    • new model_type argument allowing to specify different “pre-canned” models ("PSMA" = RoBMA-PSMA, "PP" = RoBMA-PP, "2w" = corresponding to Maier et al. (2023)),
      • +
    • +combine_data function allows combination of different effect sizes / variability measures into a common effect size measure (also used from within the RoBMA function)
      • +
    • better and improved automatic fitting procedure now enabled by default (can be turned of with autofit = FALSE)
      • +
    • prior distributions can be specified on the different scale than the supplied effect sizes (the package fits the model on Fisher’s z scale and back transforms the results back to the scale that was used for prior distributions specification, Cohen’s d by default, but both of them can be overwritten with the prior_scale and transformation arguments),
      • +
    • new prior distributions, e.g., beta or fixed weight functions,
      • +
    • and plenty of small changes to the arguments, output, and etc…
      • +
    +
    +
    +
    +

    Installation +

    +

    The package requires JAGS 4.3.2 to be installed. The release version can be installed from CRAN:

    + +

    and the development version of the package can be installed from GitHub:

    +
    +devtools::install_github("FBartos/RoBMA")
    +
    +
    +

    Example +

    +

    To illustrate the functionality of the package, we fit the RoBMA-PSMA model from the example in Bartoš, Maier, Wagenmakers, et al. (2023) to adjust for publication bias in the infamous Bem (2011) “Feeling the future” pre-cognition study. The RoBMA-PSMA model combines six selection models and PET-PEESE to adjust for publication bias. As in the pre-print, we analyze the data as described by Bem et al. (2011) in his reply to methodological critiques.

    +

    First, we load the package and the data set included in the package.

    +
    +library(RoBMA)
+#> Loading required namespace: runjags
+#> Loading required namespace: mvtnorm
+
+data("Bem2011", package = "RoBMA")
+Bem2011
+#>      d         se                                        study
+#> 1 0.25 0.10155048                  Detection of Erotic Stimuli
+#> 2 0.20 0.08246211                Avoidance of Negative Stimuli
+#> 3 0.26 0.10323629                        Retroactive Priming I
+#> 4 0.23 0.10182427                       Retroactive Priming II
+#> 5 0.22 0.10120277  Retroactive Habituation I - Negative trials
+#> 6 0.15 0.08210765 Retroactive Habituation II - Negative trials
+#> 7 0.09 0.07085372             Retroactive Induction of Boredom
+#> 8 0.19 0.10089846                     Facilitation of Recall I
+#> 9 0.42 0.14752627                    Facilitation of Recall II
    +

    Then, we fit the meta-analytic model ensemble that is composed of 36 models (the new default settings of RoBMA fitting function). These models represent all possible combinations of prior distributions for the following components:

    +
      +
    • effect size (the mean parameter μ\mu) +
        +
      • a spike at zero, representing the null hypothesis of the absence of effect
        • +
      • a standard normal distribution, representing the alternative hypothesis of the presence of effect
        • +
      +
      • +
    • heterogeneity (the heterogeneity parameter τ\tau) +
        +
      • a spike at zero, representing the null hypothesis of the absence of heterogeneity (i.e., fixed effect meta-analysis)
        • +
      • an inverse gamma distribution with shape = 1 and scale = 0.15, based on Erp et al. (2017), representing the alternative hypothesis of the presence of heterogeneity (i.e., random effect meta-analysis)
        • +
      +
      • +
    • publication bias +
        +
      • no prior distribution, representing the absence of publication bias
        • +
      • eight prior distributions specifying two two-sided weight functions, four one-sided weight functions, and PET and PEESE publication bias adjustment, representing the presence of publication bias
        • +
      +
      • +
    +

    The prior odds of the components are by default set to make all three model categories equally likely a priory (0.5 prior probability of the presence of the effect, 0.5 prior probability of the presence of the heterogeneity, and 0.5 prior probability of the presence of the publication bias). The prior model probability of the publication bias adjustment component is further split equally among the selection models represented by the six weightfunctions and the PET-PEESE models.

    +
    +fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, seed = 1)
    +

    The main summary can be obtained using the summary.RoBMA() function.

    +

    The first table shows an overview of the ensemble composition. The number of models, the prior and posterior model probabilities, and inclusion Bayes factor of the ensemble components representing the alternative hypothesis of the presence of the effect, heterogeneity, and publication bias, We can see the data show very weak evidence, barely worth mentioning, against the presence of the effect (BF10=0.479\text{BF}_{10} = 0.479 -> BF01=2.09\text{BF}_{01} = 2.09), moderate evidence for the absence of heterogeneity (BFrf=0.143\text{BF}_{\text{rf}} = 0.143 -> BFfr=7.00BF_{\text{fr}} = 7.00), and strong evidence for the presence of publication bias (BFpb=16.32\text{BF}_{\text{pb}} = 16.32).

    +

    The second table shows model-averaged estimates weighted by the individual models’ posterior probabilities. The mean estimate μ=0.037\mu =0.037, 95% CI [-0.041, 0.213], is very close to zero, corresponding to the a priory expected absence of pre-cognition. The heterogeneity estimate τ\tau has most of its probability mass around zero due to the higher support of models assuming absence of the heterogeneity. The parameters omega, representing the publication weights at each p-value interval are decreasing with increasing p-values, showing the publication bias, as well as the non zero PET and PEESE estimates.

    +
    +summary(fit)
+#> Call:
+#> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, 
+#>     seed = 1)
+#> 
+#> Robust Bayesian meta-analysis
+#> Components summary:
+#>               Models Prior prob. Post. prob. Inclusion BF
+#> Effect         18/36       0.500       0.324        0.479
+#> Heterogeneity  18/36       0.500       0.125        0.143
+#> Bias           32/36       0.500       0.942       16.323
+#> 
+#> Model-averaged estimates:
+#>                    Mean Median  0.025  0.975
+#> mu                0.037  0.000 -0.041  0.213
+#> tau               0.010  0.000  0.000  0.113
+#> omega[0,0.025]    1.000  1.000  1.000  1.000
+#> omega[0.025,0.05] 0.935  1.000  0.338  1.000
+#> omega[0.05,0.5]   0.780  1.000  0.009  1.000
+#> omega[0.5,0.95]   0.768  1.000  0.007  1.000
+#> omega[0.95,0.975] 0.786  1.000  0.007  1.000
+#> omega[0.975,1]    0.801  1.000  0.007  1.000
+#> PET               0.759  0.000  0.000  2.805
+#> PEESE             6.183  0.000  0.000 25.463
+#> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).
+#> (Estimated publication weights omega correspond to one-sided p-values.)
    +

    We can visualize the estimated mean and heterogeneity parameters using the plot.RoBMA() function. The arrows in both figures represent the point probability mass at μ=0\mu = 0 and τ=0\tau = 0, corresponding to the null hypotheses of the absence of effect and heterogeneity, both increasing in the posterior model probability from 0.5 to 0.676 and 0.875 respectively.

    +
    +plot(fit, parameter = "mu", xlim = c(-0.5, 0.5))
    +

    +
    +plot(fit, parameter = "tau")
    +

    +

    We can further visualize the publication bias adjustments of selection models, visualizing the posterior estimate of the model-averaged weightfunction that shows a sharp decrease in the publication weights of studies with p-values above the “marginal significance” (0.10) level,

    +
    +plot(fit, parameter = "weightfunction", rescale_x = TRUE)
    +

    +

    and the PET-PEESE publication bias adjustment, visualizing the individual studies’ standard errors and effect sizes as diamonds and the model-averaged estimate of the regression lines that shows a steady increase of effect sizes with increasing standard errors.

    +
    +plot(fit, parameter = "PET-PEESE", xlim = c(0, 0.25))
    +

    +

    The usual meta-analytic forest plot can be obtained with the forest() function,

    +
    +forest(fit)
    +

    +

    and visualization of the effect size estimates from models assuming presence of the effect can be obtained with the plot_models() function.

    +
    +plot_models(fit, conditional = TRUE)
    +

    +

    Apart from plotting, the individual model performance can be inspected using the summary.RoBMA() function with argument type = "models" or the overview of the individual model MCMC diagnostics can be obtained by setting type = "diagnostics" (not shown here for the lack of space).

    +

    We can also visualize the MCMC diagnostics using the diagnostics function. The function can display the chains type = "chain" / posterior sample densities type = "densities", and averaged auto-correlations type = "autocorrelation". Here, we request the chains trace plot of the μ\mu parameter of the most complex model by setting show_models = 36 (the model numbers can be obtained from the summary function with type = "models" argument).

    +
    +diagnostics(fit, parameter = "mu", type = "chains", show_models = 36)
    +

    +

    The package allows to fit highly customized models with different prior distribution functions, prior model probabilities, and provides more visualization options. See the documentation to find out more about the specific functions: RoBMA(), priors(), plot.RoBMA(). The main package functionality is also implemented within the Meta Analysis module of JASP 0.14 (JASP Team, 2020) and will be soon updated to accommodate the 2.0 version of the package.

    +
    +

    References +

    +
    +
    +Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., & Wagenmakers, E.-J. (2021). Bayesian model-averaged meta-analysis in medicine. Statistics in Medicine, 40(30), 6743–6761. https://doi.org/10.1002/sim.9170 +
    +
    +Bartoš, F., Maier, Maximilian, Quintana, D. S., & Wagenmakers, E.-J. (2022). Adjusting for publication bias in JASP and R — Selection models, PET-PEESE, and robust Bayesian meta-analysis. Advances in Methods and Practices in Psychological Science, 5(3), 1–19. https://doi.org/10.1177/25152459221109259 +
    +
    +Bartoš, F., Maier, M., Stanley, T., & Wagenmakers, E.-J. (2023). Robust Bayesian meta-regression—Model-averaged moderation analysis in the presence of publication bias. https://doi.org/10.31234/osf.io/98xb5 +
    +
    +Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & Stanley, T. D. (2023). Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods. Research Synthesis Methods, 14(1), 99–116. https://doi.org/10.1002/jrsm.1594 +
    +
    +Bartoš, F., Otte, W. M., Gronau, Q. F., Timmers, B., Ly, A., & Wagenmakers, E.-J. (2023). Empirical prior distributions for Bayesian meta-analyses of binary and time-to-event outcomes. https://doi.org/10.48550/arXiv.2306.11468 +
    +
    +Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100(3), 407–425. https://doi.org/10.1037/a0021524 +
    +
    +Bem, D. J., Utts, J., & Johnson, W. O. (2011). Must psychologists change the way they analyze their data? Journal of Personality and Social Psychology, 101(4), 716–719. https://doi.org/10.1037/a0024777 +
    +
    +Erp, S. van, Verhagen, J., Grasman, R. P., & Wagenmakers, E.-J. (2017). Estimates of between-study heterogeneity for 705 meta-analyses reported in Psychological Bulletin from 1990–2013. Journal of Open Psychology Data, 5(1), 1–5. https://doi.org/10.5334/jopd.33 +
    +
    +JASP Team. (2020). JASP (Version 0.14). https://jasp-stats.org/ +
    +
    +Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2023). Robust Bayesian meta-analysis: Addressing publication bias with model-averaging. Psychological Methods, 28(1), 107–122. https://doi.org/10.1037/met0000405 +
    +
    +
    +
    +
    + +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    +
    + + + + + + + diff --git a/docs/katex-auto.js b/docs/katex-auto.js new file mode 100644 index 00000000..20651d9f --- /dev/null +++ b/docs/katex-auto.js @@ -0,0 +1,14 @@ +// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345 +document.addEventListener("DOMContentLoaded", function () { + var mathElements = document.getElementsByClassName("math"); + var macros = []; + for (var i = 0; i < mathElements.length; i++) { + var texText = mathElements[i].firstChild; + if (mathElements[i].tagName == "SPAN") { + katex.render(texText.data, mathElements[i], { + displayMode: mathElements[i].classList.contains("display"), + throwOnError: false, + macros: macros, + fleqn: false + }); + }}}); diff --git a/docs/lightswitch.js b/docs/lightswitch.js new file mode 100644 index 00000000..9467125a --- /dev/null +++ b/docs/lightswitch.js @@ -0,0 +1,85 @@ + +/*! + * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under the Creative Commons Attribution 3.0 Unported License. + * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0. + */ + +const getStoredTheme = () => localStorage.getItem('theme') +const setStoredTheme = theme => localStorage.setItem('theme', theme) + +const getPreferredTheme = () => { + const storedTheme = getStoredTheme() + if (storedTheme) { + return storedTheme + } + + return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light' +} + +const setTheme = theme => { + if (theme === 'auto') { + document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light')) + } else { + document.documentElement.setAttribute('data-bs-theme', theme) + } +} + +function bsSetupThemeToggle () { + 'use strict' + + const showActiveTheme = (theme, focus = false) => { + var activeLabel, activeIcon; + + document.querySelectorAll('[data-bs-theme-value]').forEach(element => { + const buttonTheme = element.getAttribute('data-bs-theme-value') + const isActive = buttonTheme == theme + + element.classList.toggle('active', isActive) + element.setAttribute('aria-pressed', isActive) + + if (isActive) { + activeLabel = element.textContent; + activeIcon = element.querySelector('span').classList.value; + } + }) + + const themeSwitcher = document.querySelector('#dropdown-lightswitch') + if (!themeSwitcher) { + return + } + + themeSwitcher.setAttribute('aria-label', activeLabel) + themeSwitcher.querySelector('span').classList.value = activeIcon; + + if (focus) { + themeSwitcher.focus() + } + } + + window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => { + const storedTheme = getStoredTheme() + if (storedTheme !== 'light' && storedTheme !== 'dark') { + setTheme(getPreferredTheme()) + } + }) + + window.addEventListener('DOMContentLoaded', () => { + showActiveTheme(getPreferredTheme()) + + document + .querySelectorAll('[data-bs-theme-value]') + .forEach(toggle => { + toggle.addEventListener('click', () => { + const theme = toggle.getAttribute('data-bs-theme-value') + setTheme(theme) + setStoredTheme(theme) + showActiveTheme(theme, true) + }) + }) + }) +} + +setTheme(getPreferredTheme()); +bsSetupThemeToggle(); diff --git a/docs/link.svg b/docs/link.svg new file mode 100644 index 00000000..88ad8276 --- /dev/null +++ b/docs/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/docs/news/index.html b/docs/news/index.html new file mode 100644 index 00000000..9c55f846 --- /dev/null +++ b/docs/news/index.html @@ -0,0 +1,351 @@ + +Changelog • RoBMA + Skip to contents + + +
    +
    +
    + +

    Changelog

    + Source: NEWS.md +
    + +
    +

    version 3.2

    +
    +

    Features

    +
    • +summary_heterogeneity() function to summarize the heterogeneity of the RoBMA models (prediction interval, tau, tau^2, I^2, and H^2)
      • +
    • +check_RoBMA_convergence() function to check the convergence of the RoBMA models
      • +
    • adds informed prior distributions for binary and time-to-event outcomes via BayesTools 0.2.17
      • +
    +
    +

    Fixes

    +
    • checking and fixing the number of available cores upon loading the package (hopefully fixes some parallelization issues)
      • +
    • +update() function re-evaluates convergence checks of individual models (https://github.com/FBartos/RoBMA/issues/34)
      • +
    • typos and minor issues in the vignettes
      • +
    +
    +
    +

    version 3.1

    +
    +

    Features

    +
    • binomial-normal models for binary data via the BiBMA function
      • +
    • +NoBMA and NoBMA.reg() functions as wrappers around RoBMA RoBMA.reg() functions for simpler specification of publication bias unadjusted Bayesian model-averaged meta-analysis
      • +
    • adding odds ratios output transformation`
      • +
    • extending (instead of a complete refitting) of models via the update.RoBMA() function (only non-converged models by default or all by setting extend_all = TRUE)
      • +
    +
    +

    Fixes

    +
    • handling of non-converged models
      • +
    +
    +
    +

    version 3.0.1

    CRAN release: 2023-06-02

    +
    +

    Fixes (thanks to Don & Rens)

    +
    +
    +
    +

    version 3.0

    +
    +

    Features

    +
    • meta-regression with RoBMA.reg() function
      • +
    • posterior marginal summary and plots for the RoBMA.reg models with summary_marginal() and plot_marginal() functions
      • +
    • new vignette on hierarchical Bayesian model-averaged meta-analysis
      • +
    • new vignette on robust Bayesian model-averaged meta-regression
      • +
    • adding vignette from AMPPS tutorial
      • +
    • faster implementation of JAGS multivariate normal distribution (based on the BUGS JAGS module)
      • +
    • incorporating weight argument in the RoBMA and combine_data functions in order to pass custom likelihood weights
      • +
    • ability to use inverse square weights in the weighted meta-analysis by setting a weighted_type = "inverse_sqrt" argument
      • +
    +
    +

    Changes

    +
    • reworked interface for the hierarchical models. Prior distributions are now specified via the priors_hierarchical and priors_hierarchical_null arguments instead of priors_rho and priors_rho_null. The model summary now shows Hierarchical component summary.
      • +
    +
    +
    +

    version 2.3.2

    CRAN release: 2023-03-13

    +
    +

    Fixes

    +
    • suppressing start-up message
      • +
    • cleaning up imports
      • +
    +
    +
    +

    version 2.3.1

    CRAN release: 2022-07-16

    +
    +

    Fixes

    +
    • fixing weighted meta-analysis parameterization
      • +
    +
    +
    +

    version 2.3

    +
    +

    Features

    +
    • weighted meta-analysis by specifying study_ids argument in RoBMA() and setting weighted = TRUE. The likelihood contribution of estimates from each study is down-weighted proportionally to the number of estimates in that study. Note that this experimental feature is supposed to provide a conservative alternative for estimating RoBMA in cases with multiple estimates from a study where the multivariate option is not computationally feasible.
      • +
    +
    +
    +

    version 2.2.3

    +
    +

    Fixes

    +
    • updating the Makevars to install with R 4.2 and JAGS 4.3.1
      • +
    +
    +
    +

    version 2.2.2

    CRAN release: 2022-04-20

    +
    +

    Fixes

    +
    • updating the C++ to compile on M1 Mac
      • +
    +
    +
    +

    version 2.2.1

    CRAN release: 2022-04-06

    +
    +

    Changes

    +
    • message about the effect size scale of parameter estimates is always shown
      • +
    • compatibility with BayesTools 0.2.0+
      • +
    +
    +
    +

    version 2.2

    +
    +

    Features

    +
    • three-level meta-analysis by specifying study_ids argument in RoBMA. However, note that this is (1) an experimental feature and (2) the computational expense of fitting selection models with clustering is extreme. As of now, it is almost impossible to have more than 2-3 estimates clustered within a single study).
      • +
    +
    +
    +

    version 2.1.2

    CRAN release: 2022-01-12

    +
    +

    Fixes

    +
    • adding Windows ucrt patch (thanks to Tomas Kalibera)
      • +
    • adding BayesTools version check
      • +
    +
    +
    +

    version 2.1.1

    CRAN release: 2021-11-03

    +
    +

    Fixes

    +
    • incorrectly formatted citations in vignettes and capitalization
      • +
    +
    +

    Features

    +
    • adding informed_prior() function (from the BayesTools package) that allows specification of various informed prior distributions from the field of medicine and psychology
      • +
    • adding a vignette reproducing the example of dentine sensitivity with the informed Bayesian model-averaged meta-analysis from Bartoš et al., 2021 (open-access),
      • +
    • further reductions of fitted object size when setting save = "min" +
      • +
    +
    +
    +

    version 2.1

    +
    +

    Fixes

    +
    • more informative error message when the JAGS module fails to load
      • +
    • correcting wrong PEESE transformation for the individual models summaries (issue #12)
      • +
    • fixing error message for missing conditional PET-PEESE
      • +
    • fixing incorrect lower bound check for log(OR)
      • +
    +
    +

    Features

    +
    • adding interpret() function (issue #11)
      • +
    • adding effect size transformation via output_scale argument to plot() and plot_models() functions
      • +
    • better handling of effect size transformations and scaling - BayesTools style back-end functions with Jacobian transformations
      • +
    +
    +
    +

    version 2.0

    +

    Please notice that this is a major release that breaks backwards compatibility.

    +
    +

    Changes

    +
    • naming of the arguments specifying prior distributions for the different parameters/components of the models changed (priors_mu -> priors_effect, priors_tau -> priors_heterogeneity, and priors_omega -> priors_bias),
      • +
    • prior distributions for specifying weight functions now use a dedicated function (prior(distribution = "two.sided", parameters = ...) -> prior_weightfunction(distribution = "two.sided", parameters = ...)),
      • +
    • new dedicated function for specifying no publication bias adjustment component / no heterogeneity component (prior_none()),
      • +
    • new dedicated functions for specifying models with the PET and PEESE publication bias adjustments (prior_PET(distribution = "Cauchy", parameters = ...) and prior_PEESE(distribution = "Cauchy", parameters = ...)),
      • +
    • new default prior distribution specification for the publication bias adjustment part of the models (corresponding to the RoBMA-PSMA model from Bartoš et al., 2021 preprint),
      • +
    • new model_type argument allowing to specify different “pre-canned” models ("PSMA" = RoBMA-PSMA, "PP" = RoBMA-PP, "2w" = corresponding to Maier et al., in press , manuscript),
      • +
    • +combine_data function allows combination of different effect sizes / variability measures into a common effect size measure (also used from within the RoBMA function),
      • +
    • better and improved automatic fitting procedure now enabled by default (can be turned of with autofit = FALSE)
      • +
    • prior distributions can be specified on the different scale than the supplied effect sizes (the package fits the model on Fisher’s z scale and back transforms the results back to the scale that was used for prior distributions specification, Cohen’s d by default, but both of them can be overwritten with the prior_scale and transformation arguments),
      • +
    • new prior distributions, e.g., beta or fixed weight functions,
      • +
    • estimates from individual models are now plotted with the plot_models() function and the forest plot can be obtained with the forest() function,
      • +
    • the posterior distribution plots for the individual weights are no able supported, however, the weightfunction and the PET-PEESE publication bias adjustments can be visualized with the plot.RoBMA() function and parameter = "weightfunction" and parameter = "PET-PEESE".
      • +
    +
    +
    +

    version 1.2.1

    CRAN release: 2021-02-16

    +
    +

    Fixes

    +
    • check_setup function not working at all
      • +
    +
    +
    +

    version 1.2.0

    CRAN release: 2021-01-21

    +
    +

    Changes

    +
    • the studies’s true effects are now marginalized out of the random effects models and are no longer estimated (see Appendix A of our prerint for more details). As a results, arguments referring to the true effects are now disabled.
      • +
    • all models are now being estimated using the likelihood of effect sizes (instead of test-statistics as usually defined). We reproduced the simulation study that we used to evaluate the method performance and it achieved identical results (up to MCMC error, before marginalizing out the true effects). A big advantage of using the normal likelihood for effect sizes is a considerable speed up of the whole estimation process.
      • +
    • as a results of these two changes, the results of the models will differ to those of pre 1.2.0 version
      • +
    +
    +

    Fixes

    +
    • autofit being turn on if any control argument was specified
      • +
    +
    +
    +

    version 1.1.2

    CRAN release: 2020-12-10

    +
    +

    Fixes

    +
    • vdiffr not being used conditionally in unit tests
      • +
    +
    +
    +

    version 1.1.1

    CRAN release: 2020-11-10

    +
    +

    Fixes

    +
    • inability to fit a model without specifying a seed
      • +
    • inability to produce individual model plots due to incompatibility with the newer versions of ggplot2
      • +
    +
    +
    +

    version 1.1.0

    CRAN release: 2020-10-30

    +
    +

    Features

    +
    • parallel within and between model fitting using the parallel package with ‘parallel = TRUE’ argument
      • +
    +
    +
    +

    version 1.0.5

    CRAN release: 2020-10-13

    +
    +

    Fixes:

    +
    • models being fitted automatically until reaching R-hat lower than 1.05 without setting max_rhat and autofit control parameters
      • +
    • bug preventing to draw a bivariate plot of mu and tau
      • +
    • range for parameter estimates from individual models no containing 0 (or 1 in case of OR measured effect sizes)
      • +
    • inability to fit a model with only null mu distributions if correlation or OR measured effect sizes were specified
      • +
    • ordering of the estimated and observed effects when both of them are requested simultaneously
      • +
    • formatting of this file (NEWS.md)
      • +
    +
    +

    Improvements:

    +
    • priors plot: parameter specification, default plotting range, clearer x-axis labels in cases when the parameter is defined on transformed scale
      • +
    • parameters plots: probability scale always ends at the same spot as is the last tick on the density scale
      • +
    • adding warnings if any of the specified models has Rhat higher than 1.05 or the specified value
      • +
    • grouping the same warnings messages together
      • +
    +
    +
    +

    version 1.0.4

    CRAN release: 2020-08-07

    +
    +

    Fixes:

    +
    • inability to run models without the silent = TRUE control
      • +
    +
    +
    +

    version 1.0.3

    CRAN release: 2020-08-06

    +
    +

    Features:

    +
    • x-axis rescaling for the weight function plot (by setting ‘rescale_x = TRUE’ in the ‘plot.RoBMA’ function)
      • +
    • setting expected direction of the effect in for RoBMA function
      • +
    +
    +

    Fixes:

    +
    • marginal likelihood calculation for models with spike prior distribution on mean parameter which location was not set to 0
      • +
    • some additional error messages
      • +
    +
    +

    CRAM requested changes:

    +
    • changing information messages from ‘cat’ to ‘message’ from plot related functions
      • +
    • saving and returning the ‘par’ settings to the user defined one in the base plot functions
      • +
    +
    +
    +

    version 1.0.2

    +
    +

    Fixes:

    +
    • the summary and plot function now shows quantile based confidence intervals for individual models instead of the HPD provided before (this affects only ‘summary’/‘plot’ with ‘type = “individual”’, all other confidence intervals were quantile based before)
      • +
    +
    +
    +

    version 1.0.1

    +
    +

    Fixes:

    +
    • summary function returning median instead of mean
      • +
    +
    +
    +

    version 1.0.0 (vs the osf version)

    +
    +

    Fixes:

    +
    • incorrectly weighted theta estimates
      • +
    • models with non-zero point prior distribution incorrectly plotted using when “models” option in case that the mu parameter was transformed
      • +
    +
    +

    Additional features:

    +
    • analyzing OR
      • +
    • distributions implemented using boost library (helps with convergence issues)
      • +
    • ability to mute the non-suppressible “precision not achieved” warning messages by using “silent” = TRUE inside of the control argument
      • +
    • vignettes
      • +
    +
    +

    Notable changes:

    +
    • the way how the seed is set before model fitting (the simulation study will not be reproducible with the new version of the package)
      • +
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/pkgdown.js b/docs/pkgdown.js new file mode 100644 index 00000000..1a99c65f --- /dev/null +++ b/docs/pkgdown.js @@ -0,0 +1,162 @@ +/* http://gregfranko.com/blog/jquery-best-practices/ */ +(function($) { + $(function() { + + $('nav.navbar').headroom(); + + Toc.init({ + $nav: $("#toc"), + $scope: $("main h2, main h3, main h4, main h5, main h6") + }); + + if ($('#toc').length) { + $('body').scrollspy({ + target: '#toc', + offset: $("nav.navbar").outerHeight() + 1 + }); + } + + // Activate popovers + $('[data-bs-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); + + $('[data-bs-toggle="tooltip"]').tooltip(); + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-bs-original-title'); + element.setAttribute('data-bs-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-bs-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $("div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboard = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + } + }); + + clipboard.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboard.on('error', function(e) { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + + }); + } + + /* Search marking --------------------------*/ + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("main#main"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], + } + }); + } + + /* Search --------------------------*/ + /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ + // Initialise search index on focus + var fuse; + $("#search-input").focus(async function(e) { + if (fuse) { + return; + } + + $(e.target).addClass("loading"); + var response = await fetch($("#search-input").data("search-index")); + var data = await response.json(); + + var options = { + keys: ["what", "text", "code"], + ignoreLocation: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); + }); + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + var q; +async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + if (items.length === 0) { + items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; + } + } + callback(items); +} + $("#search-input").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.title == s.what) { + return `${s.dir} >
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    ${s.title}
    > ${s.what}`; + } else { + return `${s.dir} >
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    > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); +})(window.jQuery || window.$) + +document.addEventListener('keydown', function(event) { + // Check if the pressed key is '/' + if (event.key === '/') { + event.preventDefault(); // Prevent any default action associated with the '/' key + document.getElementById('search-input').focus(); // Set focus to the search input + } +}); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml new file mode 100644 index 00000000..e07f2107 --- /dev/null +++ b/docs/pkgdown.yml @@ -0,0 +1,15 @@ +pandoc: '3.2' +pkgdown: 2.1.1 +pkgdown_sha: ~ +articles: + CustomEnsembles: CustomEnsembles.html + HierarchicalBMA: HierarchicalBMA.html + MedicineBiBMA: MedicineBiBMA.html + MedicineBMA: MedicineBMA.html + MetaRegression: MetaRegression.html + ReproducingBMA: ReproducingBMA.html + Tutorial: Tutorial.html +last_built: 2024-12-11T16:13Z +urls: + reference: https://https://fbartos.github.io/RoBMA/reference + article: https://https://fbartos.github.io/RoBMA/articles diff --git a/docs/reference/Anderson2010.html b/docs/reference/Anderson2010.html new file mode 100644 index 00000000..8c67a831 --- /dev/null +++ b/docs/reference/Anderson2010.html @@ -0,0 +1,96 @@ + +27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria — Anderson2010 • RoBMA + Skip to contents + + +
    +
    +
    + +

    27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria

    + Source: R/datasets.R +
    Anderson2010.Rd
    +
    + +
    +

    The data set contains correlation coefficients, sample +sizes, and labels for 27 experimental studies focusing on the effect of +violent video games on aggressive behavior. The full original data can +found at https://github.com/Joe-Hilgard/Anderson-meta.

    +
    + +
    +

    Usage

    + 
    Anderson2010
    +
    + +
    +

    Format

    +

    A data.frame with 3 columns and 23 observations.

    +
    +
    +

    Value

    +

    a data.frame.

    +
    +
    +

    References

    +

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/Andrews2021.html b/docs/reference/Andrews2021.html new file mode 100644 index 00000000..7dfb8b56 --- /dev/null +++ b/docs/reference/Andrews2021.html @@ -0,0 +1,102 @@ + +36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA — Andrews2021 • RoBMA + Skip to contents + + +
    +
    +
    + +

    36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA

    + Source: R/datasets.R +
    Andrews2021.Rd
    +
    + +
    +

    The data set contains correlation coefficients r, +standard errors se, executive functioning assessment type measure, +and the mean age of the children in each study age. The original data set +assessed the effect of household chaos on child executive functions +andrews2021examiningRoBMA which was used as an +example in bartos2020adjusting;textualRoBMA.

    +
    + +
    +

    Usage

    + 
    Andrews2021
    +
    + +
    +

    Format

    +

    A data.frame with 4 columns and 36 observations.

    +
    +
    +

    Value

    +

    a data.frame.

    +
    +
    +

    References

    +

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/Bem2011.html b/docs/reference/Bem2011.html new file mode 100644 index 00000000..3064aa6c --- /dev/null +++ b/docs/reference/Bem2011.html @@ -0,0 +1,96 @@ + +9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA — Bem2011 • RoBMA + Skip to contents + + +
    +
    +
    + +

    9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA

    + Source: R/datasets.R +
    Bem2011.Rd
    +
    + +
    +

    The data set contains Cohen's d effect sizes, standard errors, +and labels for 9 experimental studies of precognition from the infamous +bem2011feeling;textualRoBMA as analyzed in his later meta-analysis +bem2011mustRoBMA.

    +
    + +
    +

    Usage

    + 
    Bem2011
    +
    + +
    +

    Format

    +

    A data.frame with 3 columns and 9 observations.

    +
    +
    +

    Value

    +

    a data.frame.

    +
    +
    +

    References

    +

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/BiBMA.html b/docs/reference/BiBMA.html new file mode 100644 index 00000000..eb4cb8e7 --- /dev/null +++ b/docs/reference/BiBMA.html @@ -0,0 +1,309 @@ + +Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data

    + Source: R/BiBMA.R +
    BiBMA.Rd
    +
    + +
    +

    BiBMA estimate a binomial-normal Bayesian +model-averaged meta-analysis. The interface allows a complete customization of +the ensemble with different prior (or list of prior) distributions +for each component.

    +
    + +
    +

    Usage

    + 
    BiBMA(
+  x1,
+  x2,
+  n1,
+  n2,
+  study_names = NULL,
+  study_ids = NULL,
+  priors_effect = prior(distribution = "student", parameters = list(location = 0, scale =
+    0.58, df = 4)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1.77,
+    scale = 0.55)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_baseline = NULL,
+  priors_baseline_null = prior_factor("beta", parameters = list(alpha = 1, beta = 1),
+    contrast = "independent"),
+  chains = 3,
+  sample = 5000,
+  burnin = 2000,
+  adapt = 500,
+  thin = 1,
+  parallel = FALSE,
+  autofit = TRUE,
+  autofit_control = set_autofit_control(),
+  convergence_checks = set_convergence_checks(),
+  save = "all",
+  seed = NULL,
+  silent = TRUE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    x1
    +

    a vector with the number of successes in the first group

    + + +
    x2
    +

    a vector with the number of successes in the second group

    + + +
    n1
    +

    a vector with the number of observations in the first group

    + + +
    n2
    +

    a vector with the number of observations in the second group

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    study_ids
    +

    an optional argument specifying dependency between the +studies (for using a multilevel model). Defaults to NULL for +studies being independent.

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "student", parameters = list(location = 0, scale = 0.58, df = 4)), +based on logOR meta-analytic estimates from the Cochrane Database of Systematic Reviews +bartos2023empiricalRoBMA.

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1.77, scale = 0.55)) that +is based on heterogeneities of logOR estimates from the Cochrane Database of Systematic Reviews +bartos2023empiricalRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_baseline
    +

    prior distributions for the alternative hypothesis about +intercepts (pi) of each study. Defaults to NULL.

    + + +
    priors_baseline_null
    +

    prior distributions for the null hypothesis about +intercepts (pi) for each study. Defaults to an independent uniform prior distribution +for each intercept prior("beta", parameters = list(alpha = 1, beta = 1), contrast = "independent").

    + + +
    chains
    +

    a number of chains of the MCMC algorithm.

    + + +
    sample
    +

    a number of sampling iterations of the MCMC algorithm. +Defaults to 5000.

    + + +
    burnin
    +

    a number of burnin iterations of the MCMC algorithm. +Defaults to 2000.

    + + +
    adapt
    +

    a number of adaptation iterations of the MCMC algorithm. +Defaults to 500.

    + + +
    thin
    +

    a thinning of the chains of the MCMC algorithm. Defaults to +1.

    + + +
    parallel
    +

    whether the individual models should be fitted in parallel. +Defaults to FALSE. The implementation is not completely stable +and might cause a connection error.

    + + +
    autofit
    +

    whether the model should be fitted until the convergence +criteria (specified in autofit_control) are satisfied. Defaults to +TRUE.

    + + +
    autofit_control
    +

    allows to pass autofit control settings with the +set_autofit_control() function. See ?set_autofit_control for +options and default settings.

    + + +
    convergence_checks
    +

    automatic convergence checks to assess the fitted +models, passed with set_convergence_checks() function. See +?set_convergence_checks for options and default settings.

    + + +
    save
    +

    whether all models posterior distributions should be kept +after obtaining a model-averaged result. Defaults to "all" which +does not remove anything. Set to "min" to significantly reduce +the size of final object, however, some model diagnostics and further +manipulation with the object will not be possible.

    + + +
    seed
    +

    a seed to be set before model fitting, marginal likelihood +computation, and posterior mixing for reproducibility of results. Defaults +to NULL - no seed is set.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    NoBMA returns an object of class 'RoBMA'.

    +
    +
    +

    Details

    +

    The BiBMA() function estimates the binomial-normal Bayesian model-averaged +meta-analysis described in bartos2023empirical;textualRoBMA. See +vignette("MedicineBiBMA", package = "RoBMA") +vignette for a reproduction of the oduwole2018honey;textualRoBMA example. +Also RoBMA() for additional details.

    +

    Generic summary.RoBMA(), print.RoBMA(), and plot.RoBMA() functions are +provided to facilitate manipulation with the ensemble. A visual check of the +individual model diagnostics can be obtained using the diagnostics() function. +The fitted model can be further updated or modified by update.RoBMA() function.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    RoBMA(), summary.RoBMA(), update.RoBMA(), check_setup()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Oduwole (2018) and reproducing the example from
+# Bartos et al. (2023) with domain specific informed prior distributions
+
+fit <- BiBMA(
+  x1          = c(5, 2),
+  x2          = c(0, 0),
+  n1          = c(35, 40),
+  n2          = c(39, 40),
+  priors_effect        = prior_informed(
+      "Acute Respiratory Infections",
+      type = "logOR", parameter = "effect"),
+  priors_heterogeneity = prior_informed(
+      "Acute Respiratory Infections",
+      type = "logOR", parameter = "heterogeneity")
+ )
+
+ summary(fit)
+
+ # produce summary on OR scale
+ summary(fit, output_scale = "OR")
+
+} # }
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/Kroupova2021.html b/docs/reference/Kroupova2021.html new file mode 100644 index 00000000..3c4b9bb9 --- /dev/null +++ b/docs/reference/Kroupova2021.html @@ -0,0 +1,105 @@ + +881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA — Kroupova2021 • RoBMA + Skip to contents + + +
    +
    +
    + +

    881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA

    + Source: R/datasets.R +
    Kroupova2021.Rd
    +
    + +
    +

    The data set contains partial correlation coefficients, standard errors, +study labels, samples sizes, type of the educational outcome, intensity of the +employment, gender of the student population, study location, study design, whether +the study controlled for endogenity, and whether the study controlled for motivation. +The original data set including additional variables and the publication can be found +at http://meta-analysis.cz/students. +(Note that some standard errors and employment intensities are missing.)

    +
    + +
    +

    Usage

    + 
    Kroupova2021
    +
    + +
    +

    Format

    +

    A data.frame with 11 columns and 881 observations.

    +
    +
    +

    Value

    +

    a data.frame.

    +
    +
    +

    References

    +

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/Lui2015.html b/docs/reference/Lui2015.html new file mode 100644 index 00000000..0b35fb6b --- /dev/null +++ b/docs/reference/Lui2015.html @@ -0,0 +1,105 @@ + +18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA — Lui2015 • RoBMA + Skip to contents + + +
    +
    +
    + +

    18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA

    + Source: R/datasets.R +
    Lui2015.Rd
    +
    + +
    +

    The data set contains correlation coefficients r, +sample sizes n, and labels for each study assessing the +relationship between acculturation mismatch (that is the result of the contrast +between the collectivist cultures of Asian and Latin immigrant groups +and the individualist culture in the United States) and intergenerational cultural +conflict lui2015intergenerationalRoBMA which was used as an +example in bartos2020adjusting;textualRoBMA.

    +
    + +
    +

    Usage

    + 
    Lui2015
    +
    + +
    +

    Format

    +

    A data.frame with 3 columns and 18 observations.

    +
    +
    +

    Value

    +

    a data.frame.

    +
    +
    +

    References

    +

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/NoBMA.html b/docs/reference/NoBMA.html new file mode 100644 index 00000000..e0c3a332 --- /dev/null +++ b/docs/reference/NoBMA.html @@ -0,0 +1,359 @@ + +Estimate a Bayesian Model-Averaged Meta-Analysis — NoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Estimate a Bayesian Model-Averaged Meta-Analysis

    + Source: R/NoBMA.R +
    NoBMA.Rd
    +
    + +
    +

    NoBMA is a wrapper around RoBMA() that can +be used to estimate a publication bias unadjusted Bayesian +model-averaged meta-analysis. The interface allows a complete customization of +the ensemble with different prior (or list of prior) distributions +for each component.

    +
    + +
    +

    Usage

    + 
    NoBMA(
+  d = NULL,
+  r = NULL,
+  logOR = NULL,
+  OR = NULL,
+  z = NULL,
+  y = NULL,
+  se = NULL,
+  v = NULL,
+  n = NULL,
+  lCI = NULL,
+  uCI = NULL,
+  t = NULL,
+  study_names = NULL,
+  study_ids = NULL,
+  data = NULL,
+  weight = NULL,
+  transformation = if (is.null(y)) "fishers_z" else "none",
+  prior_scale = if (is.null(y)) "cohens_d" else "none",
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  chains = 3,
+  sample = 5000,
+  burnin = 2000,
+  adapt = 500,
+  thin = 1,
+  parallel = FALSE,
+  autofit = TRUE,
+  autofit_control = set_autofit_control(),
+  convergence_checks = set_convergence_checks(),
+  save = "all",
+  seed = NULL,
+  silent = TRUE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    d
    +

    a vector of effect sizes measured as Cohen's d

    + + +
    r
    +

    a vector of effect sizes measured as correlations

    + + +
    logOR
    +

    a vector of effect sizes measured as log odds ratios

    + + +
    OR
    +

    a vector of effect sizes measured as odds ratios

    + + +
    z
    +

    a vector of effect sizes measured as Fisher's z

    + + +
    y
    +

    a vector of unspecified effect sizes (note that effect size +transformations are unavailable with this type of input)

    + + +
    se
    +

    a vector of standard errors of the effect sizes

    + + +
    v
    +

    a vector of variances of the effect sizes

    + + +
    n
    +

    a vector of overall sample sizes

    + + +
    lCI
    +

    a vector of lower bounds of confidence intervals

    + + +
    uCI
    +

    a vector of upper bounds of confidence intervals

    + + +
    t
    +

    a vector of t/z-statistics

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    study_ids
    +

    an optional argument specifying dependency between the +studies (for using a multilevel model). Defaults to NULL for +studies being independent.

    + + +
    data
    +

    a data object created by the combine_data function. This is +an alternative input entry to specifying the d, r, y, etc... +directly. I.e., RoBMA function does not allow passing a data.frame and +referencing to the columns.

    + + +
    weight
    +

    specifies likelihood weights of the individual estimates. +Notes that this is an untested experimental feature.

    + + +
    transformation
    +

    transformation to be applied to the supplied +effect sizes before fitting the individual models. Defaults to +"fishers_z". We highly recommend using "fishers_z" +transformation since it is the only variance stabilizing measure +and does not bias PET and PEESE style models. The other options are +"cohens_d", correlation coefficient "r" and "logOR". +Supplying "none" will treat the effect sizes as unstandardized and +refrain from any transformations.

    + + +
    prior_scale
    +

    an effect size scale used to define priors. Defaults to "cohens_d". +Other options are "fishers_z", correlation coefficient "r", +and "logOR". The prior scale does not need to match the effect sizes measure - +the samples from prior distributions are internally transformed to match the +transformation of the data. The prior_scale corresponds to +the effect size scale of default output, but can be changed within the summary function.

    + + +
    model_type
    +

    string specifying the RoBMA ensemble. Defaults to NULL. +The other options are "PSMA", "PP", and "2w" which override +settings passed to the priors_effect, priors_heterogeneity, +priors_effect, priors_effect_null, priors_heterogeneity_null, +priors_bias_null, and priors_effect. See details for more information +about the different model types.

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +a standard normal distribution +prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15)) that +is based on heterogeneities estimates from psychology erp2017estimatesRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_hierarchical
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows +users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. +Note that this is an experimental feature and see News for more details. Defaults to a beta distribution +prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

    + + +
    priors_hierarchical_null
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    chains
    +

    a number of chains of the MCMC algorithm.

    + + +
    sample
    +

    a number of sampling iterations of the MCMC algorithm. +Defaults to 5000.

    + + +
    burnin
    +

    a number of burnin iterations of the MCMC algorithm. +Defaults to 2000.

    + + +
    adapt
    +

    a number of adaptation iterations of the MCMC algorithm. +Defaults to 500.

    + + +
    thin
    +

    a thinning of the chains of the MCMC algorithm. Defaults to +1.

    + + +
    parallel
    +

    whether the individual models should be fitted in parallel. +Defaults to FALSE. The implementation is not completely stable +and might cause a connection error.

    + + +
    autofit
    +

    whether the model should be fitted until the convergence +criteria (specified in autofit_control) are satisfied. Defaults to +TRUE.

    + + +
    autofit_control
    +

    allows to pass autofit control settings with the +set_autofit_control() function. See ?set_autofit_control for +options and default settings.

    + + +
    convergence_checks
    +

    automatic convergence checks to assess the fitted +models, passed with set_convergence_checks() function. See +?set_convergence_checks for options and default settings.

    + + +
    save
    +

    whether all models posterior distributions should be kept +after obtaining a model-averaged result. Defaults to "all" which +does not remove anything. Set to "min" to significantly reduce +the size of final object, however, some model diagnostics and further +manipulation with the object will not be possible.

    + + +
    seed
    +

    a seed to be set before model fitting, marginal likelihood +computation, and posterior mixing for reproducibility of results. Defaults +to NULL - no seed is set.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    NoBMA returns an object of class 'RoBMA'.

    +
    +
    +

    Details

    +

    See RoBMA() for more details.

    +

    Note that these default prior distributions are relatively wide and more informed +prior distributions for testing for the presence of moderation should be considered.

    +
    +
    +

    See also

    +

    RoBMA(), summary.RoBMA(), update.RoBMA(), check_setup()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/NoBMA.reg.html b/docs/reference/NoBMA.reg.html new file mode 100644 index 00000000..57ba701f --- /dev/null +++ b/docs/reference/NoBMA.reg.html @@ -0,0 +1,374 @@ + +Estimate a Bayesian Model-Averaged Meta-Regression — NoBMA.reg • RoBMA + Skip to contents + + +
    +
    +
    + +

    Estimate a Bayesian Model-Averaged Meta-Regression

    + Source: R/NoBMA.R +
    NoBMA.reg.Rd
    +
    + +
    +

    NoBMA.reg is a wrapper around RoBMA.reg() that can +be used to estimate a publication bias unadjusted Bayesian +model-averaged meta-regression. The interface allows a complete customization of +the ensemble with different prior (or list of prior) distributions +for each component.

    +
    + +
    +

    Usage

    + 
    NoBMA.reg(
+  formula,
+  data,
+  test_predictors = TRUE,
+  study_names = NULL,
+  study_ids = NULL,
+  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
+  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
+  standardize_predictors = TRUE,
+  priors = NULL,
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
+  prior_covariates_null = prior("spike", parameters = list(location = 0)),
+  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
+    contrast = "meandif"),
+  prior_factors_null = prior_factor("spike", parameters = list(location = 0), contrast =
+    "meandif"),
+  chains = 3,
+  sample = 5000,
+  burnin = 2000,
+  adapt = 500,
+  thin = 1,
+  parallel = FALSE,
+  autofit = TRUE,
+  autofit_control = set_autofit_control(),
+  convergence_checks = set_convergence_checks(),
+  save = "all",
+  seed = NULL,
+  silent = TRUE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    formula
    +

    a formula for the meta-regression model

    + + +
    data
    +

    a data.frame containing the data for the meta-regression. Note that the +column names have to correspond to the effect sizes (d, logOR, OR, +r, z), a measure of sampling variability (se, v, n, +lCI, uCI, t), and the predictors. +See combine_data() for a complete list of reserved names and additional information +about specifying input data.

    + + +
    test_predictors
    +

    vector of predictor names to test for the presence +of moderation (i.e., assigned both the null and alternative prior distributions). +Defaults to TRUE, all predictors are tested using the default +prior distributions (i.e., prior_covariates, +prior_covariates_null, prior_factors, and +prior_factors_null). To only estimate +and adjust for the effect of predictors use FALSE. If +priors is specified, any settings in test_predictors +is overridden.

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    study_ids
    +

    an optional argument specifying dependency between the +studies (for using a multilevel model). Defaults to NULL for +studies being independent.

    + + +
    transformation
    +

    transformation to be applied to the supplied +effect sizes before fitting the individual models. Defaults to +"fishers_z". We highly recommend using "fishers_z" +transformation since it is the only variance stabilizing measure +and does not bias PET and PEESE style models. The other options are +"cohens_d", correlation coefficient "r" and "logOR". +Supplying "none" will treat the effect sizes as unstandardized and +refrain from any transformations.

    + + +
    prior_scale
    +

    an effect size scale used to define priors. Defaults to "cohens_d". +Other options are "fishers_z", correlation coefficient "r", +and "logOR". The prior scale does not need to match the effect sizes measure - +the samples from prior distributions are internally transformed to match the +transformation of the data. The prior_scale corresponds to +the effect size scale of default output, but can be changed within the summary function.

    + + +
    standardize_predictors
    +

    whether continuous predictors should be standardized prior to +estimating the model. Defaults to TRUE.

    + + +
    priors
    +

    named list of prior distributions for each predictor +(with names corresponding to the predictors). It allows users to +specify both the null and alternative hypothesis prior distributions +for each predictor by assigning the corresponding element of the named +list with another named list (with "null" and +"alt"). +If only one prior is specified for a given parameter, it is +assumed to correspond to the alternative hypotheses and the default null +hypothesis is specified (i.e., prior_covariates_null or +prior_factors_null). +If a named list with only one named prior distribution is provided (either +"null" or "alt"), only this prior distribution is used and no +default distribution is filled in. +Parameters without specified prior distributions are assumed to be only adjusted +for using the default alternative hypothesis prior distributions (i.e., +prior_covariates or prior_factors). +If priors is specified, test_predictors is ignored.

    + + +
    model_type
    +

    string specifying the RoBMA ensemble. Defaults to NULL. +The other options are "PSMA", "PP", and "2w" which override +settings passed to the priors_effect, priors_heterogeneity, +priors_effect, priors_effect_null, priors_heterogeneity_null, +priors_bias_null, and priors_effect. See details for more information +about the different model types.

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +a standard normal distribution +prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15)) that +is based on heterogeneities estimates from psychology erp2017estimatesRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_hierarchical
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows +users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. +Note that this is an experimental feature and see News for more details. Defaults to a beta distribution +prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

    + + +
    priors_hierarchical_null
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    prior_covariates
    +

    a prior distributions for the regression parameter +of continuous covariates on the effect size under the alternative hypothesis +(unless set explicitly in priors). Defaults to a relatively wide normal +distribution prior(distribution = "normal", parameters = list(mean = 0, sd = 0.25)).

    + + +
    prior_covariates_null
    +

    a prior distributions for the regression parameter +of continuous covariates on the effect size under the null hypothesis +(unless set explicitly in priors). Defaults to a no effect +prior("spike", parameters = list(location = 0)).

    + + +
    prior_factors
    +

    a prior distributions for the regression parameter +of categorical covariates on the effect size under the alternative hypothesis +(unless set explicitly in priors). Defaults to a relatively wide +multivariate normal distribution specifying differences from the mean contrasts +prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25), contrast = "meandif").

    + + +
    prior_factors_null
    +

    a prior distributions for the regression parameter +of categorical covariates on the effect size under the null hypothesis +(unless set explicitly in priors). Defaults to a no effect +prior("spike", parameters = list(location = 0)).

    + + +
    chains
    +

    a number of chains of the MCMC algorithm.

    + + +
    sample
    +

    a number of sampling iterations of the MCMC algorithm. +Defaults to 5000.

    + + +
    burnin
    +

    a number of burnin iterations of the MCMC algorithm. +Defaults to 2000.

    + + +
    adapt
    +

    a number of adaptation iterations of the MCMC algorithm. +Defaults to 500.

    + + +
    thin
    +

    a thinning of the chains of the MCMC algorithm. Defaults to +1.

    + + +
    parallel
    +

    whether the individual models should be fitted in parallel. +Defaults to FALSE. The implementation is not completely stable +and might cause a connection error.

    + + +
    autofit
    +

    whether the model should be fitted until the convergence +criteria (specified in autofit_control) are satisfied. Defaults to +TRUE.

    + + +
    autofit_control
    +

    allows to pass autofit control settings with the +set_autofit_control() function. See ?set_autofit_control for +options and default settings.

    + + +
    convergence_checks
    +

    automatic convergence checks to assess the fitted +models, passed with set_convergence_checks() function. See +?set_convergence_checks for options and default settings.

    + + +
    save
    +

    whether all models posterior distributions should be kept +after obtaining a model-averaged result. Defaults to "all" which +does not remove anything. Set to "min" to significantly reduce +the size of final object, however, some model diagnostics and further +manipulation with the object will not be possible.

    + + +
    seed
    +

    a seed to be set before model fitting, marginal likelihood +computation, and posterior mixing for reproducibility of results. Defaults +to NULL - no seed is set.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    NoBMA.reg returns an object of class 'RoBMA'.

    +
    +
    +

    Details

    +

    See RoBMA.reg() for more details.

    +

    Note that these default prior distributions are relatively wide and more informed +prior distributions for testing for the presence of moderation should be considered.

    +
    +
    +

    See also

    +

    RoBMA(), RoBMA.reg(), summary.RoBMA(), update.RoBMA(), check_setup()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/OR2d.html b/docs/reference/OR2d.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/OR2d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/OR2logOR.html b/docs/reference/OR2logOR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/OR2logOR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/OR2r.html b/docs/reference/OR2r.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/OR2r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/OR2z.html b/docs/reference/OR2z.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/OR2z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/Poulsen2006.html b/docs/reference/Poulsen2006.html new file mode 100644 index 00000000..6f677ebf --- /dev/null +++ b/docs/reference/Poulsen2006.html @@ -0,0 +1,99 @@ + +5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity — Poulsen2006 • RoBMA + Skip to contents + + +
    +
    +
    + +

    5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity

    + Source: R/datasets.R +
    Poulsen2006.Rd
    +
    + +
    +

    The data set contains Cohen's d effect sizes, standard errors, +and labels for 5 studies assessing the tactile outcome from a meta-analysis of +the effect of potassium-containing toothpaste on dentine hypersensitivity +poulsen2006potassiumRoBMA which was used as an example in +bartos2021bayesian;textualRoBMA.

    +
    + +
    +

    Usage

    + 
    Poulsen2006
    +
    + +
    +

    Format

    +

    A data.frame with 3 columns and 5 observations.

    +
    +
    +

    Value

    +

    a data.frame.

    +
    +
    +

    References

    +

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/RoBMA-package.html b/docs/reference/RoBMA-package.html new file mode 100644 index 00000000..8ce0227c --- /dev/null +++ b/docs/reference/RoBMA-package.html @@ -0,0 +1,104 @@ + +RoBMA: Robust Bayesian meta-analysis — RoBMA-package • RoBMA + Skip to contents + + +
    +
    +
    + +

    RoBMA: Robust Bayesian meta-analysis

    + Source: R/RoBMA-package.R +
    RoBMA-package.Rd
    +
    + +
    +

    RoBMA: Bayesian model-averaged meta-analysis with adjustments for publication +bias and ability to specify informed prior distributions and draw inference with +inclusion Bayes factors.

    +
    + + +
    +

    User guide

    +

    See bartos2021no;textualRoBMA, +maier2020robust;textualRoBMA, and +bartos2020adjusting;textualRoBMA for details regarding the RoBMA +methodology.

    +

    More details regarding customization of the model ensembles are provided in the +Reproducing BMA, +BMA in Medicine, and +Fitting Custom Meta-Analytic Ensembles +vignettes. Please, use the "Issues" section in the GitHub repository to ask any +further questions.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    Useful links:

    +
    +
    +

    Author

    +

    František Bartoš f.bartos96@gmail.com

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/RoBMA.get_option.html b/docs/reference/RoBMA.get_option.html new file mode 100644 index 00000000..3b68981b --- /dev/null +++ b/docs/reference/RoBMA.get_option.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/RoBMA.html b/docs/reference/RoBMA.html new file mode 100644 index 00000000..c645d36e --- /dev/null +++ b/docs/reference/RoBMA.html @@ -0,0 +1,477 @@ + +Estimate a Robust Bayesian Meta-Analysis — RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Estimate a Robust Bayesian Meta-Analysis

    + Source: R/RoBMA.R +
    RoBMA.Rd
    +
    + +
    +

    RoBMA is used to estimate a robust Bayesian +meta-analysis. The interface allows a complete customization of +the ensemble with different prior (or list of prior) distributions +for each component.

    +
    + +
    +

    Usage

    + 
    RoBMA(
+  d = NULL,
+  r = NULL,
+  logOR = NULL,
+  OR = NULL,
+  z = NULL,
+  y = NULL,
+  se = NULL,
+  v = NULL,
+  n = NULL,
+  lCI = NULL,
+  uCI = NULL,
+  t = NULL,
+  study_names = NULL,
+  study_ids = NULL,
+  data = NULL,
+  weight = NULL,
+  transformation = if (is.null(y)) "fishers_z" else "none",
+  prior_scale = if (is.null(y)) "cohens_d" else "none",
+  effect_direction = "positive",
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
+    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
+    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
+    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
+    
+    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
+    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
+    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
+    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_bias_null = prior_none(),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  chains = 3,
+  sample = 5000,
+  burnin = 2000,
+  adapt = 500,
+  thin = 1,
+  parallel = FALSE,
+  autofit = TRUE,
+  autofit_control = set_autofit_control(),
+  convergence_checks = set_convergence_checks(),
+  save = "all",
+  seed = NULL,
+  silent = TRUE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    d
    +

    a vector of effect sizes measured as Cohen's d

    + + +
    r
    +

    a vector of effect sizes measured as correlations

    + + +
    logOR
    +

    a vector of effect sizes measured as log odds ratios

    + + +
    OR
    +

    a vector of effect sizes measured as odds ratios

    + + +
    z
    +

    a vector of effect sizes measured as Fisher's z

    + + +
    y
    +

    a vector of unspecified effect sizes (note that effect size +transformations are unavailable with this type of input)

    + + +
    se
    +

    a vector of standard errors of the effect sizes

    + + +
    v
    +

    a vector of variances of the effect sizes

    + + +
    n
    +

    a vector of overall sample sizes

    + + +
    lCI
    +

    a vector of lower bounds of confidence intervals

    + + +
    uCI
    +

    a vector of upper bounds of confidence intervals

    + + +
    t
    +

    a vector of t/z-statistics

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    study_ids
    +

    an optional argument specifying dependency between the +studies (for using a multilevel model). Defaults to NULL for +studies being independent.

    + + +
    data
    +

    a data object created by the combine_data function. This is +an alternative input entry to specifying the d, r, y, etc... +directly. I.e., RoBMA function does not allow passing a data.frame and +referencing to the columns.

    + + +
    weight
    +

    specifies likelihood weights of the individual estimates. +Notes that this is an untested experimental feature.

    + + +
    transformation
    +

    transformation to be applied to the supplied +effect sizes before fitting the individual models. Defaults to +"fishers_z". We highly recommend using "fishers_z" +transformation since it is the only variance stabilizing measure +and does not bias PET and PEESE style models. The other options are +"cohens_d", correlation coefficient "r" and "logOR". +Supplying "none" will treat the effect sizes as unstandardized and +refrain from any transformations.

    + + +
    prior_scale
    +

    an effect size scale used to define priors. Defaults to "cohens_d". +Other options are "fishers_z", correlation coefficient "r", +and "logOR". The prior scale does not need to match the effect sizes measure - +the samples from prior distributions are internally transformed to match the +transformation of the data. The prior_scale corresponds to +the effect size scale of default output, but can be changed within the summary function.

    + + +
    effect_direction
    +

    the expected direction of the effect. Correctly specifying +the expected direction of the effect is crucial for one-sided selection models, +as they specify cut-offs using one-sided p-values. Defaults to "positive" +(another option is "negative").

    + + +
    model_type
    +

    string specifying the RoBMA ensemble. Defaults to NULL. +The other options are "PSMA", "PP", and "2w" which override +settings passed to the priors_effect, priors_heterogeneity, +priors_effect, priors_effect_null, priors_heterogeneity_null, +priors_bias_null, and priors_effect. See details for more information +about the different model types.

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +a standard normal distribution +prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15)) that +is based on heterogeneities estimates from psychology erp2017estimatesRoBMA.

    + + +
    priors_bias
    +

    list of prior distributions for the publication bias adjustment +component that will be treated as belonging to the alternative hypothesis. +Defaults to list( +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.10)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.025, 0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.5)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), + steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), +prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), + prior_weights = 1/4), +prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), + prior_weights = 1/4) +), corresponding to the RoBMA-PSMA model introduce by bartos2021no;textualRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_bias_null
    +

    list of prior weight functions for the omega parameter +that will be treated as belonging to the null hypothesis. Defaults no publication +bias adjustment, prior_none().

    + + +
    priors_hierarchical
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows +users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. +Note that this is an experimental feature and see News for more details. Defaults to a beta distribution +prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

    + + +
    priors_hierarchical_null
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    chains
    +

    a number of chains of the MCMC algorithm.

    + + +
    sample
    +

    a number of sampling iterations of the MCMC algorithm. +Defaults to 5000.

    + + +
    burnin
    +

    a number of burnin iterations of the MCMC algorithm. +Defaults to 2000.

    + + +
    adapt
    +

    a number of adaptation iterations of the MCMC algorithm. +Defaults to 500.

    + + +
    thin
    +

    a thinning of the chains of the MCMC algorithm. Defaults to +1.

    + + +
    parallel
    +

    whether the individual models should be fitted in parallel. +Defaults to FALSE. The implementation is not completely stable +and might cause a connection error.

    + + +
    autofit
    +

    whether the model should be fitted until the convergence +criteria (specified in autofit_control) are satisfied. Defaults to +TRUE.

    + + +
    autofit_control
    +

    allows to pass autofit control settings with the +set_autofit_control() function. See ?set_autofit_control for +options and default settings.

    + + +
    convergence_checks
    +

    automatic convergence checks to assess the fitted +models, passed with set_convergence_checks() function. See +?set_convergence_checks for options and default settings.

    + + +
    save
    +

    whether all models posterior distributions should be kept +after obtaining a model-averaged result. Defaults to "all" which +does not remove anything. Set to "min" to significantly reduce +the size of final object, however, some model diagnostics and further +manipulation with the object will not be possible.

    + + +
    seed
    +

    a seed to be set before model fitting, marginal likelihood +computation, and posterior mixing for reproducibility of results. Defaults +to NULL - no seed is set.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    RoBMA returns an object of class 'RoBMA'.

    +
    +
    +

    Details

    +

    The default settings of the RoBMA 2.0 package corresponds to the RoBMA-PSMA +ensemble proposed by bartos2021no;textualRoBMA. The previous versions +of the package (i.e., RoBMA < 2.0) used specifications proposed by +maier2020robust;textualRoBMA (this specification can be easily +obtained by setting model_type = "2w". The RoBMA-PP specification from +bartos2021no;textualRoBMA can be obtained by setting +model_type = "PP".

    +

    The vignette("CustomEnsembles", package = "RoBMA") +and vignette("ReproducingBMA", package = "RoBMA") +vignettes describe how to use RoBMA() to fit custom meta-analytic ensembles (see prior(), +prior_weightfunction(), prior_PET(), and prior_PEESE() for more information about prior +distributions).

    +

    The RoBMA function first generates models from a combination of the +provided priors for each of the model parameters. Then, the individual models +are fitted using autorun.jags function. A marginal likelihood +is computed using bridge_sampler function. The individual +models are then combined into an ensemble using the posterior model probabilities +using BayesTools package.

    +

    Generic summary.RoBMA(), print.RoBMA(), and plot.RoBMA() functions are +provided to facilitate manipulation with the ensemble. A visual check of the +individual model diagnostics can be obtained using the diagnostics() function. +The fitted model can be further updated or modified by update.RoBMA() function.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    summary.RoBMA(), update.RoBMA(), check_setup()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Bem 2011 and fitting the default (RoBMA-PSMA) model
+fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study)
+
+# in order to speed up the process, we can turn the parallelization on
+fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, parallel = TRUE)
+
+# we can get a quick overview of the model coefficients just by printing the model
+fit
+
+# a more detailed overview using the summary function (see '?summary.RoBMA' for all options)
+summary(fit)
+
+# the model-averaged effect size estimate can be visualized using the plot function
+# (see ?plot.RoBMA for all options)
+plot(fit, parameter = "mu")
+
+# forest plot can be obtained with the forest function (see ?forest for all options)
+forest(fit)
+
+# plot of the individual model estimates can be obtained with the plot_models function
+#  (see ?plot_models for all options)
+plot_models(fit)
+
+# diagnostics for the individual parameters in individual models can be obtained using diagnostics
+# function (see 'diagnostics' for all options)
+diagnostics(fit, parameter = "mu", type = "chains")
+
+# the RoBMA-PP can be fitted with addition of the 'model_type' argument
+fit_PP <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, model_type = "PP")
+
+# as well as the original version of RoBMA (with two weightfunctions)
+fit_original <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study,
+                      model_type = "2w")
+
+# or different prior distribution for the effect size (e.g., a half-normal distribution)
+# (see 'vignette("CustomEnsembles")' for a detailed guide on specifying a custom model ensemble)
+fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study,
+             priors_effect = prior("normal", parameters = list(0, 1),
+                                   truncation = list(0, Inf)))
+} # }
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/RoBMA.options.html b/docs/reference/RoBMA.options.html new file mode 100644 index 00000000..3b68981b --- /dev/null +++ b/docs/reference/RoBMA.options.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/RoBMA.package.html b/docs/reference/RoBMA.package.html new file mode 100644 index 00000000..3b3c8ac8 --- /dev/null +++ b/docs/reference/RoBMA.package.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/RoBMA.reg.html b/docs/reference/RoBMA.reg.html new file mode 100644 index 00000000..d4fac124 --- /dev/null +++ b/docs/reference/RoBMA.reg.html @@ -0,0 +1,462 @@ + +Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg • RoBMA + Skip to contents + + +
    +
    +
    + +

    Estimate a Robust Bayesian Meta-Analysis Meta-Regression

    + Source: R/RoBMA-reg.R +
    RoBMA.reg.Rd
    +
    + +
    +

    RoBMA is used to estimate a robust Bayesian +meta-regression. The interface allows a complete customization of +the ensemble with different prior (or list of prior) distributions +for each component.

    +
    + +
    +

    Usage

    + 
    RoBMA.reg(
+  formula,
+  data,
+  test_predictors = TRUE,
+  study_names = NULL,
+  study_ids = NULL,
+  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
+  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
+  standardize_predictors = TRUE,
+  effect_direction = "positive",
+  priors = NULL,
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
+    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
+    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
+    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
+    
+    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
+    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
+    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
+    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_bias_null = prior_none(),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
+  prior_covariates_null = prior("spike", parameters = list(location = 0)),
+  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
+    contrast = "meandif"),
+  prior_factors_null = prior_factor("spike", parameters = list(location = 0), contrast =
+    "meandif"),
+  chains = 3,
+  sample = 5000,
+  burnin = 2000,
+  adapt = 500,
+  thin = 1,
+  parallel = FALSE,
+  autofit = TRUE,
+  autofit_control = set_autofit_control(),
+  convergence_checks = set_convergence_checks(),
+  save = "all",
+  seed = NULL,
+  silent = TRUE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    formula
    +

    a formula for the meta-regression model

    + + +
    data
    +

    a data.frame containing the data for the meta-regression. Note that the +column names have to correspond to the effect sizes (d, logOR, OR, +r, z), a measure of sampling variability (se, v, n, +lCI, uCI, t), and the predictors. +See combine_data() for a complete list of reserved names and additional information +about specifying input data.

    + + +
    test_predictors
    +

    vector of predictor names to test for the presence +of moderation (i.e., assigned both the null and alternative prior distributions). +Defaults to TRUE, all predictors are tested using the default +prior distributions (i.e., prior_covariates, +prior_covariates_null, prior_factors, and +prior_factors_null). To only estimate +and adjust for the effect of predictors use FALSE. If +priors is specified, any settings in test_predictors +is overridden.

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    study_ids
    +

    an optional argument specifying dependency between the +studies (for using a multilevel model). Defaults to NULL for +studies being independent.

    + + +
    transformation
    +

    transformation to be applied to the supplied +effect sizes before fitting the individual models. Defaults to +"fishers_z". We highly recommend using "fishers_z" +transformation since it is the only variance stabilizing measure +and does not bias PET and PEESE style models. The other options are +"cohens_d", correlation coefficient "r" and "logOR". +Supplying "none" will treat the effect sizes as unstandardized and +refrain from any transformations.

    + + +
    prior_scale
    +

    an effect size scale used to define priors. Defaults to "cohens_d". +Other options are "fishers_z", correlation coefficient "r", +and "logOR". The prior scale does not need to match the effect sizes measure - +the samples from prior distributions are internally transformed to match the +transformation of the data. The prior_scale corresponds to +the effect size scale of default output, but can be changed within the summary function.

    + + +
    standardize_predictors
    +

    whether continuous predictors should be standardized prior to +estimating the model. Defaults to TRUE.

    + + +
    effect_direction
    +

    the expected direction of the effect. Correctly specifying +the expected direction of the effect is crucial for one-sided selection models, +as they specify cut-offs using one-sided p-values. Defaults to "positive" +(another option is "negative").

    + + +
    priors
    +

    named list of prior distributions for each predictor +(with names corresponding to the predictors). It allows users to +specify both the null and alternative hypothesis prior distributions +for each predictor by assigning the corresponding element of the named +list with another named list (with "null" and +"alt"). +If only one prior is specified for a given parameter, it is +assumed to correspond to the alternative hypotheses and the default null +hypothesis is specified (i.e., prior_covariates_null or +prior_factors_null). +If a named list with only one named prior distribution is provided (either +"null" or "alt"), only this prior distribution is used and no +default distribution is filled in. +Parameters without specified prior distributions are assumed to be only adjusted +for using the default alternative hypothesis prior distributions (i.e., +prior_covariates or prior_factors). +If priors is specified, test_predictors is ignored.

    + + +
    model_type
    +

    string specifying the RoBMA ensemble. Defaults to NULL. +The other options are "PSMA", "PP", and "2w" which override +settings passed to the priors_effect, priors_heterogeneity, +priors_effect, priors_effect_null, priors_heterogeneity_null, +priors_bias_null, and priors_effect. See details for more information +about the different model types.

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +a standard normal distribution +prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15)) that +is based on heterogeneities estimates from psychology erp2017estimatesRoBMA.

    + + +
    priors_bias
    +

    list of prior distributions for the publication bias adjustment +component that will be treated as belonging to the alternative hypothesis. +Defaults to list( +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.10)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.025, 0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.5)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), + steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), +prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), + prior_weights = 1/4), +prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), + prior_weights = 1/4) +), corresponding to the RoBMA-PSMA model introduce by bartos2021no;textualRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_bias_null
    +

    list of prior weight functions for the omega parameter +that will be treated as belonging to the null hypothesis. Defaults no publication +bias adjustment, prior_none().

    + + +
    priors_hierarchical
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows +users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. +Note that this is an experimental feature and see News for more details. Defaults to a beta distribution +prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

    + + +
    priors_hierarchical_null
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    prior_covariates
    +

    a prior distributions for the regression parameter +of continuous covariates on the effect size under the alternative hypothesis +(unless set explicitly in priors). Defaults to a relatively wide normal +distribution prior(distribution = "normal", parameters = list(mean = 0, sd = 0.25)).

    + + +
    prior_covariates_null
    +

    a prior distributions for the regression parameter +of continuous covariates on the effect size under the null hypothesis +(unless set explicitly in priors). Defaults to a no effect +prior("spike", parameters = list(location = 0)).

    + + +
    prior_factors
    +

    a prior distributions for the regression parameter +of categorical covariates on the effect size under the alternative hypothesis +(unless set explicitly in priors). Defaults to a relatively wide +multivariate normal distribution specifying differences from the mean contrasts +prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25), contrast = "meandif").

    + + +
    prior_factors_null
    +

    a prior distributions for the regression parameter +of categorical covariates on the effect size under the null hypothesis +(unless set explicitly in priors). Defaults to a no effect +prior("spike", parameters = list(location = 0)).

    + + +
    chains
    +

    a number of chains of the MCMC algorithm.

    + + +
    sample
    +

    a number of sampling iterations of the MCMC algorithm. +Defaults to 5000.

    + + +
    burnin
    +

    a number of burnin iterations of the MCMC algorithm. +Defaults to 2000.

    + + +
    adapt
    +

    a number of adaptation iterations of the MCMC algorithm. +Defaults to 500.

    + + +
    thin
    +

    a thinning of the chains of the MCMC algorithm. Defaults to +1.

    + + +
    parallel
    +

    whether the individual models should be fitted in parallel. +Defaults to FALSE. The implementation is not completely stable +and might cause a connection error.

    + + +
    autofit
    +

    whether the model should be fitted until the convergence +criteria (specified in autofit_control) are satisfied. Defaults to +TRUE.

    + + +
    autofit_control
    +

    allows to pass autofit control settings with the +set_autofit_control() function. See ?set_autofit_control for +options and default settings.

    + + +
    convergence_checks
    +

    automatic convergence checks to assess the fitted +models, passed with set_convergence_checks() function. See +?set_convergence_checks for options and default settings.

    + + +
    save
    +

    whether all models posterior distributions should be kept +after obtaining a model-averaged result. Defaults to "all" which +does not remove anything. Set to "min" to significantly reduce +the size of final object, however, some model diagnostics and further +manipulation with the object will not be possible.

    + + +
    seed
    +

    a seed to be set before model fitting, marginal likelihood +computation, and posterior mixing for reproducibility of results. Defaults +to NULL - no seed is set.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    RoBMA.reg returns an object of class 'RoBMA.reg'.

    +
    +
    +

    Details

    +

    The vignette("/MetaRegression", package = "RoBMA") +vignette describes how to use RoBMA.reg() function to fit Bayesian meta-regression ensembles. See +bartos2023robust;textualRoBMA for more details about the methodology and +RoBMA() for more details about the function options.

    +

    The RoBMA.reg function first generates models from a combination of the +provided priors for each of the model parameters. Then, the individual models +are fitted using autorun.jags function. A marginal likelihood +is computed using bridge_sampler function. The individual +models are then combined into an ensemble using the posterior model probabilities +using BayesTools package.

    +

    Generic summary.RoBMA(), print.RoBMA(), and plot.RoBMA() functions are +provided to facilitate manipulation with the ensemble. A visual check of the +individual model diagnostics can be obtained using the diagnostics() function. +The fitted model can be further updated or modified by update.RoBMA() function. +Estimated marginal means can be computed by marginal_summary() function and +visualized by the marginal_plot() function.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    RoBMA() summary.RoBMA(), update.RoBMA(), check_setup.reg()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Andrews et al. (2021) and reproducing the example from
+# Bartos et al. (2024) with measure and age covariate.
+
+ # note the the Andrews2021 data.frame columns identify the effect size "r" and
+ # the standard error "se" of the effect size that are used to estimate the model
+ fit_RoBMA <- RoBMA.reg(~ measure + age, data = Andrews2021, parallel = TRUE, seed = 1)
+
+ # summarize the results
+ summary(fit_RoBMA, output_scale = "r")
+
+ # compute effect size estimates for each group
+ marginal_summary(fit_RoBMA, output_scale = "r")
+
+ # visualize the effect size estimates for each group
+ marginal_plot(fit_RoBMA, parameter = "measure", output_scale = "r", lwd = 2)
+} # }
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/RoBMA_control.html b/docs/reference/RoBMA_control.html new file mode 100644 index 00000000..3a02ca48 --- /dev/null +++ b/docs/reference/RoBMA_control.html @@ -0,0 +1,163 @@ + +Control MCMC fitting process — RoBMA_control • RoBMA + Skip to contents + + +
    +
    +
    + +

    Control MCMC fitting process

    + Source: R/check-input-and-settings.R +
    RoBMA_control.Rd
    +
    + +
    +

    Controls settings for the autofit +process of the MCMC JAGS sampler (specifies termination +criteria), and values for the convergence checks.

    +
    + +
    +

    Usage

    + 
    set_autofit_control(
+  max_Rhat = 1.05,
+  min_ESS = 500,
+  max_error = NULL,
+  max_SD_error = NULL,
+  max_time = list(time = 60, unit = "mins"),
+  sample_extend = 1000,
+  restarts = 10
+)
+
+set_convergence_checks(
+  max_Rhat = 1.05,
+  min_ESS = 500,
+  max_error = NULL,
+  max_SD_error = NULL,
+  remove_failed = FALSE,
+  balance_probability = TRUE
+)
    +
    + +
    +

    Arguments

    + + +
    max_Rhat
    +

    maximum value of the R-hat diagnostic. +Defaults to 1.05.

    + + +
    min_ESS
    +

    minimum estimated sample size. +Defaults to 500.

    + + +
    max_error
    +

    maximum value of the MCMC error. +Defaults to NULL. Be aware that PEESE publication bias +adjustment can have estimates on different scale than the rest of +the output, resulting in relatively large max MCMC error.

    + + +
    max_SD_error
    +

    maximum value of the proportion of MCMC error +of the estimated SD of the parameter. +Defaults to NULL.

    + + +
    max_time
    +

    list with the time and unit specifying the maximum +autofitting process per model. Passed to difftime function +(possible units are "secs", "mins", "hours", "days", "weeks", "years"). +Defaults to list(time = 60, unit = "mins").

    + + +
    sample_extend
    +

    number of samples to extend the fitting process if +the criteria are not satisfied. +Defaults to 1000.

    + + +
    restarts
    +

    number of times new initial values should be generated in case a +model fails to initialize. Defaults to 10.

    + + +
    remove_failed
    +

    whether models not satisfying the convergence checks should +be removed from the inference. Defaults to FALSE - only a warning is raised.

    + + +
    balance_probability
    +

    whether prior model probability should be balanced +across the combinations of models with the same H0/H1 for effect / heterogeneity / bias +in the case of non-convergence. Defaults to TRUE.

    + +
    +
    +

    Value

    +

    set_autofit_control returns a list of autofit control settings +and set_convergence_checks returns a list of convergence checks settings.

    +
    +
    +

    See also

    +

    RoBMA, update.RoBMA

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/RoBMA_options.html b/docs/reference/RoBMA_options.html new file mode 100644 index 00000000..288384ce --- /dev/null +++ b/docs/reference/RoBMA_options.html @@ -0,0 +1,99 @@ + +Options for the RoBMA package — RoBMA_options • RoBMA + Skip to contents + + +
    +
    +
    + +

    Options for the RoBMA package

    + Source: R/utilities.R +
    RoBMA_options.Rd
    +
    + +
    +

    A placeholder object and functions for the RoBMA package. +(adapted from the runjags R package).

    +
    + +
    +

    Usage

    + 
    RoBMA.options(...)
+
+RoBMA.get_option(name)
    +
    + +
    +

    Arguments

    + + +
    ...
    +

    named option(s) to change - for a list of available options, see +details below.

    + + +
    name
    +

    the name of the option to get the current value of - for a list of +available options, see details below.

    + +
    +
    +

    Value

    +

    The current value of all available RoBMA options (after applying any +changes specified) is returned invisibly as a named list.

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/RoBMA_package.html b/docs/reference/RoBMA_package.html new file mode 100644 index 00000000..3b3c8ac8 --- /dev/null +++ b/docs/reference/RoBMA_package.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/check_RoBMA.html b/docs/reference/check_RoBMA.html new file mode 100644 index 00000000..17db5819 --- /dev/null +++ b/docs/reference/check_RoBMA.html @@ -0,0 +1,98 @@ + +Check fitted RoBMA object for errors and warnings — check_RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Check fitted RoBMA object for errors and warnings

    + Source: R/tools.R +
    check_RoBMA.Rd
    +
    + +
    +

    Checks fitted RoBMA object +for warnings and errors and prints them to the +console.

    +
    + +
    +

    Usage

    + 
    check_RoBMA(fit)
+
+check_RoBMA_convergence(fit)
    +
    + +
    +

    Arguments

    + + +
    fit
    +

    a fitted RoBMA object.

    + +
    +
    +

    Value

    +

    check_RoBMA returns a vector of error and +warning messages. check_RoBMA_convergence returns +a logical vector indicating whether the models have +converged.

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/check_RoBMA_convergence.html b/docs/reference/check_RoBMA_convergence.html new file mode 100644 index 00000000..925a83cc --- /dev/null +++ b/docs/reference/check_RoBMA_convergence.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/check_setup.BiBMA.html b/docs/reference/check_setup.BiBMA.html new file mode 100644 index 00000000..4f8a62c8 --- /dev/null +++ b/docs/reference/check_setup.BiBMA.html @@ -0,0 +1,162 @@ + +Prints summary of "BiBMA.reg" ensemble implied by the specified priors and formula — check_setup.BiBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Prints summary of "BiBMA.reg" ensemble implied by the specified priors and formula

    + Source: R/check-input-and-settings.R +
    check_setup.BiBMA.Rd
    +
    + +
    +

    check_setup prints summary of "RoBMA.reg" ensemble +implied by the specified prior distributions. It is useful for checking +the ensemble configuration prior to fitting all of the models.

    +
    + +
    +

    Usage

    + 
    check_setup.BiBMA(
+  priors_effect = prior(distribution = "student", parameters = list(location = 0, scale =
+    0.58, df = 4)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1.77,
+    scale = 0.55)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_baseline = NULL,
+  priors_baseline_null = prior_factor("beta", parameters = list(alpha = 1, beta = 1),
+    contrast = "independent"),
+  models = FALSE,
+  silent = FALSE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "student", parameters = list(location = 0, scale = 0.58, df = 4)), +based on logOR meta-analytic estimates from the Cochrane Database of Systematic Reviews +bartos2023empiricalRoBMA.

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1.77, scale = 0.55)) that +is based on heterogeneities of logOR estimates from the Cochrane Database of Systematic Reviews +bartos2023empiricalRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_baseline
    +

    prior distributions for the alternative hypothesis about +intercepts (pi) of each study. Defaults to NULL.

    + + +
    priors_baseline_null
    +

    prior distributions for the null hypothesis about +intercepts (pi) for each study. Defaults to an independent uniform prior distribution +for each intercept prior("beta", parameters = list(alpha = 1, beta = 1), contrast = "independent").

    + + +
    models
    +

    should the models' details be printed.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    check_setup.reg invisibly returns list of summary tables.

    +
    +
    +

    See also

    +

    check_setup() BiBMA()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/check_setup.RoBMA.html b/docs/reference/check_setup.RoBMA.html new file mode 100644 index 00000000..97d62dda --- /dev/null +++ b/docs/reference/check_setup.RoBMA.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/check_setup.RoBMA.reg.html b/docs/reference/check_setup.RoBMA.reg.html new file mode 100644 index 00000000..7ac98bd8 --- /dev/null +++ b/docs/reference/check_setup.RoBMA.reg.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/check_setup.html b/docs/reference/check_setup.html new file mode 100644 index 00000000..bec8473f --- /dev/null +++ b/docs/reference/check_setup.html @@ -0,0 +1,236 @@ + +Prints summary of "RoBMA" ensemble implied by the specified priors — check_setup • RoBMA + Skip to contents + + +
    +
    +
    + +

    Prints summary of "RoBMA" ensemble implied by the specified priors

    + Source: R/check-input-and-settings.R +
    check_setup.Rd
    +
    + +
    +

    check_setup prints summary of "RoBMA" ensemble +implied by the specified prior distributions. It is useful for checking +the ensemble configuration prior to fitting all of the models.

    +
    + +
    +

    Usage

    + 
    check_setup(
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
+    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
+    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
+    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
+    
+    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
+    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
+    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
+    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_bias_null = prior_none(),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  models = FALSE,
+  silent = FALSE
+)
+
+check_setup.RoBMA(
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
+    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
+    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
+    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
+    
+    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
+    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
+    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
+    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_bias_null = prior_none(),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  models = FALSE,
+  silent = FALSE
+)
    +
    + +
    +

    Arguments

    + + +
    model_type
    +

    string specifying the RoBMA ensemble. Defaults to NULL. +The other options are "PSMA", "PP", and "2w" which override +settings passed to the priors_effect, priors_heterogeneity, +priors_effect, priors_effect_null, priors_heterogeneity_null, +priors_bias_null, and priors_effect. See details for more information +about the different model types.

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +a standard normal distribution +prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15)) that +is based on heterogeneities estimates from psychology erp2017estimatesRoBMA.

    + + +
    priors_bias
    +

    list of prior distributions for the publication bias adjustment +component that will be treated as belonging to the alternative hypothesis. +Defaults to list( +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.10)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.025, 0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.5)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), + steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), +prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), + prior_weights = 1/4), +prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), + prior_weights = 1/4) +), corresponding to the RoBMA-PSMA model introduce by bartos2021no;textualRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_bias_null
    +

    list of prior weight functions for the omega parameter +that will be treated as belonging to the null hypothesis. Defaults no publication +bias adjustment, prior_none().

    + + +
    priors_hierarchical
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows +users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. +Note that this is an experimental feature and see News for more details. Defaults to a beta distribution +prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

    + + +
    priors_hierarchical_null
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    models
    +

    should the models' details be printed.

    + + +
    silent
    +

    do not print the results.

    + +
    +
    +

    Value

    +

    check_setup invisibly returns list of summary tables.

    +
    +
    +

    See also

    +

    check_setup.reg() RoBMA()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/check_setup.reg.html b/docs/reference/check_setup.reg.html new file mode 100644 index 00000000..9f7dbd62 --- /dev/null +++ b/docs/reference/check_setup.reg.html @@ -0,0 +1,444 @@ + +Prints summary of "RoBMA.reg" ensemble implied by the specified priors and formula — check_setup.reg • RoBMA + Skip to contents + + +
    +
    +
    + +

    Prints summary of "RoBMA.reg" ensemble implied by the specified priors and formula

    + Source: R/check-input-and-settings.R +
    check_setup.reg.Rd
    +
    + +
    +

    check_setup prints summary of "RoBMA.reg" ensemble +implied by the specified prior distributions. It is useful for checking +the ensemble configuration prior to fitting all of the models.

    +

    check_setup prints summary of "RoBMA.reg" ensemble +implied by the specified prior distributions. It is useful for checking +the ensemble configuration prior to fitting all of the models.

    +
    + +
    +

    Usage

    + 
    check_setup.reg(
+  formula,
+  data,
+  test_predictors = TRUE,
+  study_names = NULL,
+  study_ids = NULL,
+  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
+  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
+  standardize_predictors = TRUE,
+  effect_direction = "positive",
+  priors = NULL,
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
+    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
+    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
+    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
+    
+    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
+    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
+    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
+    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_bias_null = prior_none(),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
+  prior_covariates_null = prior("spike", parameters = list(location = 0)),
+  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
+    contrast = "meandif"),
+  prior_factors_null = prior("spike", parameters = list(location = 0)),
+  models = FALSE,
+  silent = FALSE,
+  ...
+)
+
+check_setup.RoBMA.reg(
+  formula,
+  data,
+  test_predictors = TRUE,
+  study_names = NULL,
+  study_ids = NULL,
+  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
+  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
+  standardize_predictors = TRUE,
+  effect_direction = "positive",
+  priors = NULL,
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
+    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
+    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
+    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
+    
+    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
+    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
+    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
+    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_bias_null = prior_none(),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
+  prior_covariates_null = prior("spike", parameters = list(location = 0)),
+  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
+    contrast = "meandif"),
+  prior_factors_null = prior("spike", parameters = list(location = 0)),
+  models = FALSE,
+  silent = FALSE,
+  ...
+)
+
+check_setup.reg(
+  formula,
+  data,
+  test_predictors = TRUE,
+  study_names = NULL,
+  study_ids = NULL,
+  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
+  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
+  standardize_predictors = TRUE,
+  effect_direction = "positive",
+  priors = NULL,
+  model_type = NULL,
+  priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
+  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
+    scale = 0.15)),
+  priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
+    list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+    prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
+    1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
+    c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), 
+    
+    prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
+    1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
+    "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
+    prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
+    truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
+    parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
+  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
+  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
+    0)),
+  priors_bias_null = prior_none(),
+  priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
+  priors_hierarchical_null = NULL,
+  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
+  prior_covariates_null = prior("spike", parameters = list(location = 0)),
+  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
+    contrast = "meandif"),
+  prior_factors_null = prior("spike", parameters = list(location = 0)),
+  models = FALSE,
+  silent = FALSE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    formula
    +

    a formula for the meta-regression model

    + + +
    data
    +

    a data.frame containing the data for the meta-regression. Note that the +column names have to correspond to the effect sizes (d, logOR, OR, +r, z), a measure of sampling variability (se, v, n, +lCI, uCI, t), and the predictors. +See combine_data() for a complete list of reserved names and additional information +about specifying input data.

    + + +
    test_predictors
    +

    vector of predictor names to test for the presence +of moderation (i.e., assigned both the null and alternative prior distributions). +Defaults to TRUE, all predictors are tested using the default +prior distributions (i.e., prior_covariates, +prior_covariates_null, prior_factors, and +prior_factors_null). To only estimate +and adjust for the effect of predictors use FALSE. If +priors is specified, any settings in test_predictors +is overridden.

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    study_ids
    +

    an optional argument specifying dependency between the +studies (for using a multilevel model). Defaults to NULL for +studies being independent.

    + + +
    transformation
    +

    transformation to be applied to the supplied +effect sizes before fitting the individual models. Defaults to +"fishers_z". We highly recommend using "fishers_z" +transformation since it is the only variance stabilizing measure +and does not bias PET and PEESE style models. The other options are +"cohens_d", correlation coefficient "r" and "logOR". +Supplying "none" will treat the effect sizes as unstandardized and +refrain from any transformations.

    + + +
    prior_scale
    +

    an effect size scale used to define priors. Defaults to "cohens_d". +Other options are "fishers_z", correlation coefficient "r", +and "logOR". The prior scale does not need to match the effect sizes measure - +the samples from prior distributions are internally transformed to match the +transformation of the data. The prior_scale corresponds to +the effect size scale of default output, but can be changed within the summary function.

    + + +
    standardize_predictors
    +

    whether continuous predictors should be standardized prior to +estimating the model. Defaults to TRUE.

    + + +
    effect_direction
    +

    the expected direction of the effect. Correctly specifying +the expected direction of the effect is crucial for one-sided selection models, +as they specify cut-offs using one-sided p-values. Defaults to "positive" +(another option is "negative").

    + + +
    priors
    +

    named list of prior distributions for each predictor +(with names corresponding to the predictors). It allows users to +specify both the null and alternative hypothesis prior distributions +for each predictor by assigning the corresponding element of the named +list with another named list (with "null" and +"alt"). +If only one prior is specified for a given parameter, it is +assumed to correspond to the alternative hypotheses and the default null +hypothesis is specified (i.e., prior_covariates_null or +prior_factors_null). +If a named list with only one named prior distribution is provided (either +"null" or "alt"), only this prior distribution is used and no +default distribution is filled in. +Parameters without specified prior distributions are assumed to be only adjusted +for using the default alternative hypothesis prior distributions (i.e., +prior_covariates or prior_factors). +If priors is specified, test_predictors is ignored.

    + + +
    model_type
    +

    string specifying the RoBMA ensemble. Defaults to NULL. +The other options are "PSMA", "PP", and "2w" which override +settings passed to the priors_effect, priors_heterogeneity, +priors_effect, priors_effect_null, priors_heterogeneity_null, +priors_bias_null, and priors_effect. See details for more information +about the different model types.

    + + +
    priors_effect
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +a standard normal distribution +prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

    + + +
    priors_heterogeneity
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. Defaults to +prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15)) that +is based on heterogeneities estimates from psychology erp2017estimatesRoBMA.

    + + +
    priors_bias
    +

    list of prior distributions for the publication bias adjustment +component that will be treated as belonging to the alternative hypothesis. +Defaults to list( +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.10)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), + steps = c(0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.025, 0.05)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), + steps = c(0.05, 0.5)), prior_weights = 1/12), +prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), + steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), +prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), + prior_weights = 1/4), +prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), + prior_weights = 1/4) +), corresponding to the RoBMA-PSMA model introduce by bartos2021no;textualRoBMA.

    + + +
    priors_effect_null
    +

    list of prior distributions for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero, +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_heterogeneity_null
    +

    list of prior distributions for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. Defaults to +a point null hypotheses at zero (a fixed effect meta-analytic models), +prior(distribution = "point", parameters = list(location = 0)).

    + + +
    priors_bias_null
    +

    list of prior weight functions for the omega parameter +that will be treated as belonging to the null hypothesis. Defaults no publication +bias adjustment, prior_none().

    + + +
    priors_hierarchical
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows +users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. +Note that this is an experimental feature and see News for more details. Defaults to a beta distribution +prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

    + + +
    priors_hierarchical_null
    +

    list of prior distributions for the correlation of random effects +(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    prior_covariates
    +

    a prior distributions for the regression parameter +of continuous covariates on the effect size under the alternative hypothesis +(unless set explicitly in priors). Defaults to a relatively wide normal +distribution prior(distribution = "normal", parameters = list(mean = 0, sd = 0.25)).

    + + +
    prior_covariates_null
    +

    a prior distributions for the regression parameter +of continuous covariates on the effect size under the null hypothesis +(unless set explicitly in priors). Defaults to a no effect +prior("spike", parameters = list(location = 0)).

    + + +
    prior_factors
    +

    a prior distributions for the regression parameter +of categorical covariates on the effect size under the alternative hypothesis +(unless set explicitly in priors). Defaults to a relatively wide +multivariate normal distribution specifying differences from the mean contrasts +prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25), contrast = "meandif").

    + + +
    prior_factors_null
    +

    a prior distributions for the regression parameter +of categorical covariates on the effect size under the null hypothesis +(unless set explicitly in priors). Defaults to a no effect +prior("spike", parameters = list(location = 0)).

    + + +
    models
    +

    should the models' details be printed.

    + + +
    silent
    +

    do not print the results.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    check_setup.reg invisibly returns list of summary tables.

    +

    check_setup.reg invisibly returns list of summary tables.

    +
    +
    +

    See also

    +

    check_setup() RoBMA.reg()

    +

    check_setup() RoBMA.reg()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/combine_data.html b/docs/reference/combine_data.html new file mode 100644 index 00000000..db5b219b --- /dev/null +++ b/docs/reference/combine_data.html @@ -0,0 +1,227 @@ + +Combines different effect sizes into a common metric — combine_data • RoBMA + Skip to contents + + +
    +
    +
    + +

    Combines different effect sizes into a common metric

    + Source: R/transformations.R +
    combine_data.Rd
    +
    + +
    +

    combine_data combines different effect sizes +into a common measure specified in transformation. Either +a data.frame data with columns named corresponding to the +arguments or vectors with individual values can be passed.

    +
    + +
    +

    Usage

    + 
    combine_data(
+  d = NULL,
+  r = NULL,
+  z = NULL,
+  logOR = NULL,
+  OR = NULL,
+  t = NULL,
+  y = NULL,
+  se = NULL,
+  v = NULL,
+  n = NULL,
+  lCI = NULL,
+  uCI = NULL,
+  study_names = NULL,
+  study_ids = NULL,
+  weight = NULL,
+  data = NULL,
+  transformation = "fishers_z",
+  return_all = FALSE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    d
    +

    a vector of effect sizes measured as Cohen's d

    + + +
    r
    +

    a vector of effect sizes measured as correlations

    + + +
    z
    +

    a vector of effect sizes measured as Fisher's z

    + + +
    logOR
    +

    a vector of effect sizes measured as log odds ratios

    + + +
    OR
    +

    a vector of effect sizes measured as odds ratios

    + + +
    t
    +

    a vector of t/z-statistics

    + + +
    y
    +

    a vector of unspecified effect sizes (note that effect size +transformations are unavailable with this type of input)

    + + +
    se
    +

    a vector of standard errors of the effect sizes

    + + +
    v
    +

    a vector of variances of the effect sizes

    + + +
    n
    +

    a vector of overall sample sizes

    + + +
    lCI
    +

    a vector of lower bounds of confidence intervals

    + + +
    uCI
    +

    a vector of upper bounds of confidence intervals

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    study_ids
    +

    an optional argument specifying dependency between the +studies (for using a multilevel model). Defaults to NULL for +studies being independent.

    + + +
    weight
    +

    specifies likelihood weights of the individual estimates. +Notes that this is an untested experimental feature.

    + + +
    data
    +

    a data frame with column names corresponding to the +variable names used to supply data individually

    + + +
    transformation
    +

    transformation to be applied to the supplied +effect sizes before fitting the individual models. Defaults to +"fishers_z". We highly recommend using "fishers_z" +transformation since it is the only variance stabilizing measure +and does not bias PET and PEESE style models. The other options are +"cohens_d", correlation coefficient "r" and "logOR". +Supplying "none" will treat the effect sizes as unstandardized and +refrain from any transformations.

    + + +
    return_all
    +

    whether data frame containing all filled values should be +returned. Defaults to FALSE

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    combine_data returns a data.frame.

    +
    +
    +

    Details

    +

    The aim of the function is to combine different, already calculated, +effect size measures. In order to obtain effect size measures from raw values, +e.g, mean differences, standard deviations, and sample sizes, use +escalc function.

    +

    The function checks the input values and in transforming the input into a common +effect size measure in the following fashion:

    1. obtains missing standard errors by squaring variances

      2. +

    2. obtains missing standard errors from confidence intervals (after transformation to +Fisher's z scale for d and r).

      3. +

    3. obtains missing sample sizes (or standard errors for logOR) from t-statistics +and effect sizes

      4. +

    4. obtains missing standard errors from sample sizes and effect sizes

      5. +

    5. obtains missing sample sizes from standard errors and effect sizes

      6. +

    6. obtains missing t-statistics from sample sizes and effect sizes +(or standard errors and effect sizes for logOR)

      7. +

    7. changes the effect sizes direction to be positive

      8. +

    8. transforms effect sizes into the common effect size

      9. +

    9. transforms standard errors into the common metric

      10. +

    If the transforms is NULL or an unstandardized effect size y is +supplied, steps 4-9 are skipped.

    +
    +
    +

    See also

    +

    RoBMA(), check_setup(), effect_sizes(), standard_errors(), and sample_sizes()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/contr.independent.html b/docs/reference/contr.independent.html new file mode 100644 index 00000000..b70526ee --- /dev/null +++ b/docs/reference/contr.independent.html @@ -0,0 +1,110 @@ + +Independent contrast matrix — contr.independent • RoBMA + Skip to contents + + +
    +
    +
    + +

    Independent contrast matrix

    + Source: R/priors.R +
    contr.independent.Rd
    +
    + +
    +

    Return a matrix of independent contrasts – a level for each term.

    +
    + +
    +

    Usage

    + 
    contr.independent(n, contrasts = TRUE)
    +
    + +
    +

    Arguments

    + + +
    n
    +

    a vector of levels for a factor, or the number of levels

    + + +
    contrasts
    +

    logical indicating whether contrasts should be computed

    + +
    +
    +

    Value

    +

    A matrix with n rows and k columns, with k = n if contrasts = TRUE and k = n +if contrasts = FALSE.

    +
    +
    +

    References

    +

    +
    + +
    +

    Examples

    + 
    contr.independent(c(1, 2))
+#>      [,1] [,2]
+#> [1,]    1    0
+#> [2,]    0    1
+contr.independent(c(1, 2, 3))
+#>      [,1] [,2] [,3]
+#> [1,]    1    0    0
+#> [2,]    0    1    0
+#> [3,]    0    0    1
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/contr.meandif.html b/docs/reference/contr.meandif.html new file mode 100644 index 00000000..68b5c76d --- /dev/null +++ b/docs/reference/contr.meandif.html @@ -0,0 +1,128 @@ + +Mean difference contrast matrix — contr.meandif • RoBMA + Skip to contents + + +
    +
    +
    + +

    Mean difference contrast matrix

    + Source: R/priors.R +
    contr.meandif.Rd
    +
    + +
    +

    Return a matrix of mean difference contrasts. +This is an adjustment to the contr.orthonormal that ascertains that the prior +distributions on difference between the gran mean and factor level are identical independent +of the number of factor levels (which does not hold for the orthonormal contrast). Furthermore, +the contrast is re-scaled so the specified prior distribution exactly corresponds to the prior +distribution on difference between each factor level and the grand mean – this is approximately +twice the scale of contr.orthonormal.

    +
    + +
    +

    Usage

    + 
    contr.meandif(n, contrasts = TRUE)
    +
    + +
    +

    Arguments

    + + +
    n
    +

    a vector of levels for a factor, or the number of levels

    + + +
    contrasts
    +

    logical indicating whether contrasts should be computed

    + +
    +
    +

    Value

    +

    A matrix with n rows and k columns, with k = n - 1 if contrasts = TRUE and k = n +if contrasts = FALSE.

    +
    +
    +

    References

    +

    +
    + +
    +

    Examples

    + 
    contr.meandif(c(1, 2))
+#>      [,1]
+#> [1,]   -1
+#> [2,]    1
+contr.meandif(c(1, 2, 3))
+#>            [,1] [,2]
+#> [1,]  0.0000000  1.0
+#> [2,] -0.8660254 -0.5
+#> [3,]  0.8660254 -0.5
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/contr.orthonormal.html b/docs/reference/contr.orthonormal.html new file mode 100644 index 00000000..1541c18f --- /dev/null +++ b/docs/reference/contr.orthonormal.html @@ -0,0 +1,116 @@ + +Orthornomal contrast matrix — contr.orthonormal • RoBMA + Skip to contents + + +
    +
    +
    + +

    Orthornomal contrast matrix

    + Source: R/priors.R +
    contr.orthonormal.Rd
    +
    + +
    +

    Return a matrix of orthornomal contrasts. +Code is based on stanova::contr.bayes and corresponding to description +by rouder2012default;textualBayesTools

    +
    + +
    +

    Usage

    + 
    contr.orthonormal(n, contrasts = TRUE)
    +
    + +
    +

    Arguments

    + + +
    n
    +

    a vector of levels for a factor, or the number of levels

    + + +
    contrasts
    +

    logical indicating whether contrasts should be computed

    + +
    +
    +

    Value

    +

    A matrix with n rows and k columns, with k = n - 1 if contrasts = TRUE and k = n +if contrasts = FALSE.

    +
    +
    +

    References

    +

    +
    + +
    +

    Examples

    + 
    contr.orthonormal(c(1, 2))
+#>            [,1]
+#> [1,] -0.7071068
+#> [2,]  0.7071068
+contr.orthonormal(c(1, 2, 3))
+#>            [,1]       [,2]
+#> [1,]  0.0000000  0.8164966
+#> [2,] -0.7071068 -0.4082483
+#> [3,]  0.7071068 -0.4082483
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/d2OR.html b/docs/reference/d2OR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/d2OR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/d2logOR.html b/docs/reference/d2logOR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/d2logOR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/d2r.html b/docs/reference/d2r.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/d2r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/d2z.html b/docs/reference/d2z.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/d2z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/diagnostics.html b/docs/reference/diagnostics.html new file mode 100644 index 00000000..33226c0c --- /dev/null +++ b/docs/reference/diagnostics.html @@ -0,0 +1,218 @@ + +Checks a fitted RoBMA object — diagnostics • RoBMA + Skip to contents + + +
    +
    +
    + +

    Checks a fitted RoBMA object

    + Source: R/diagnostics.R +
    diagnostics.Rd
    +
    + +
    +

    diagnostics creates visual +checks of individual models convergence. Numerical +overview of individual models can be obtained by +summary(object, type = "models", diagnostics = TRUE), +or even more detailed information by +summary(object, type = "individual").

    +
    + +
    +

    Usage

    + 
    diagnostics(
+  fit,
+  parameter,
+  type,
+  plot_type = "base",
+  show_models = NULL,
+  lags = 30,
+  title = is.null(show_models) | length(show_models) > 1,
+  ...
+)
+
+diagnostics_autocorrelation(
+  fit,
+  parameter = NULL,
+  plot_type = "base",
+  show_models = NULL,
+  lags = 30,
+  title = is.null(show_models) | length(show_models) > 1,
+  ...
+)
+
+diagnostics_trace(
+  fit,
+  parameter = NULL,
+  plot_type = "base",
+  show_models = NULL,
+  title = is.null(show_models) | length(show_models) > 1,
+  ...
+)
+
+diagnostics_density(
+  fit,
+  parameter = NULL,
+  plot_type = "base",
+  show_models = NULL,
+  title = is.null(show_models) | length(show_models) > 1,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    fit
    +

    a fitted RoBMA object

    + + +
    parameter
    +

    a parameter to be plotted. Either +"mu", "tau", "omega", "PET", +or "PEESE".

    + + +
    type
    +

    type of MCMC diagnostic to be plotted. +Options are "chains" for the chains' trace plots, +"autocorrelation" for autocorrelation of the +chains, and "densities" for the overlaying +densities of the individual chains. Can be abbreviated to +first letters.

    + + +
    plot_type
    +

    whether to use a base plot "base" +or ggplot2 "ggplot" for plotting. Defaults to +"base".

    + + +
    show_models
    +

    MCMC diagnostics of which models should be +plotted. Defaults to NULL which plots MCMC diagnostics +for a specified parameter for every model that is part of the +ensemble.

    + + +
    lags
    +

    number of lags to be shown for +type = "autocorrelation". Defaults to 30.

    + + +
    title
    +

    whether the model number should be displayed in title. +Defaults to TRUE when more than one model is selected.

    + + +
    ...
    +

    additional arguments to be passed to +par if plot_type = "base".

    + +
    +
    +

    Value

    +

    diagnostics returns either NULL if plot_type = "base" +or an object/list of objects (depending on the number of parameters to be plotted) +of class 'ggplot2' if plot_type = "ggplot2".

    +
    +
    +

    Details

    +

    The visualization functions are based on +stan_plot function and its color schemes.

    +
    +
    +

    See also

    +

    RoBMA(), summary.RoBMA()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Anderson et al. 2010 and fitting the default model
+# (note that the model can take a while to fit)
+fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels)
+
+### ggplot2 version of all of the plots can be obtained by adding 'model_type = "ggplot"
+# diagnostics function allows to visualize diagnostics of a fitted RoBMA object, for example,
+# the trace plot for the mean parameter in each model model
+diagnostics(fit, parameter = "mu", type = "chain")
+
+# in order to show the trace plot only for the 11th model, add show_models parameter
+diagnostics(fit, parameter = "mu", type = "chain", show_models = 11)
+
+# furthermore, the autocorrelations
+diagnostics(fit, parameter = "mu", type = "autocorrelation")
+
+# and overlying densities for each plot can also be visualize
+diagnostics(fit, parameter = "mu", type = "densities")
+} # }
+
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/diagnostics_autocorrelation.html b/docs/reference/diagnostics_autocorrelation.html new file mode 100644 index 00000000..1c15a038 --- /dev/null +++ b/docs/reference/diagnostics_autocorrelation.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/diagnostics_density.html b/docs/reference/diagnostics_density.html new file mode 100644 index 00000000..1c15a038 --- /dev/null +++ b/docs/reference/diagnostics_density.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/diagnostics_trace.html b/docs/reference/diagnostics_trace.html new file mode 100644 index 00000000..1c15a038 --- /dev/null +++ b/docs/reference/diagnostics_trace.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/dwnorm.html b/docs/reference/dwnorm.html new file mode 100644 index 00000000..7115b8cc --- /dev/null +++ b/docs/reference/dwnorm.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/effect_sizes.html b/docs/reference/effect_sizes.html new file mode 100644 index 00000000..7b1ffca4 --- /dev/null +++ b/docs/reference/effect_sizes.html @@ -0,0 +1,155 @@ + +Effect size transformations — effect_sizes • RoBMA + Skip to contents + + +
    +
    +
    + +

    Effect size transformations

    + Source: R/transformations.R +
    effect_sizes.Rd
    +
    + +
    +

    Functions for transforming between different +effect size measures.

    +
    + +
    +

    Usage

    + 
    d2r(d)
+
+d2z(d)
+
+d2logOR(d)
+
+d2OR(d)
+
+r2d(r)
+
+r2z(r)
+
+r2logOR(r)
+
+r2OR(r)
+
+z2r(z)
+
+z2d(z)
+
+z2logOR(z)
+
+z2OR(z)
+
+logOR2r(logOR)
+
+logOR2z(logOR)
+
+logOR2d(logOR)
+
+logOR2OR(logOR)
+
+OR2r(OR)
+
+OR2z(OR)
+
+OR2logOR(OR)
+
+OR2d(OR)
    +
    + +
    +

    Arguments

    + + +
    d
    +

    Cohen's d.

    + + +
    r
    +

    correlation coefficient.

    + + +
    z
    +

    Fisher's z.

    + + +
    logOR
    +

    log(odds ratios).

    + + +
    OR
    +

    offs ratios.

    + +
    +
    +

    Details

    +

    All transformations are based on +borenstein2011introductionRoBMA. In case that +a direct transformation is not available, the transformations +are chained to provide the effect size of interest.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    standard_errors(), sample_sizes()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
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    +
    +
    + +

    Forest plot for a RoBMA object

    + Source: R/plots.R +
    forest.Rd
    +
    + +
    +

    forest creates a forest plot for +a "RoBMA" object.

    +
    + +
    +

    Usage

    + 
    forest(
+  x,
+  conditional = FALSE,
+  plot_type = "base",
+  output_scale = NULL,
+  order = NULL,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a fitted RoBMA object

    + + +
    conditional
    +

    whether conditional estimates should be +plotted. Defaults to FALSE which plots the model-averaged +estimates. Note that both "weightfunction" and +"PET-PEESE" are always ignoring the other type of +publication bias adjustment.

    + + +
    plot_type
    +

    whether to use a base plot "base" +or ggplot2 "ggplot" for plotting. Defaults to +"base".

    + + +
    output_scale
    +

    transform the effect sizes and the meta-analytic +effect size estimate to a different scale. Defaults to NULL +which returns the same scale as the model was estimated on.

    + + +
    order
    +

    order of the studies. Defaults to NULL - +ordering as supplied to the fitting function. Studies +can be ordered either "increasing" or "decreasing" by +effect size, or by labels "alphabetical".

    + + +
    ...
    +

    list of additional graphical arguments +to be passed to the plotting function. Supported arguments +are lwd, lty, col, col.fill, +xlab, ylab, main, xlim, ylim +to adjust the line thickness, line type, line color, fill color, +x-label, y-label, title, x-axis range, and y-axis range +respectively.

    + +
    +
    +

    Value

    +

    forest returns either NULL if plot_type = "base" +or an object object of class 'ggplot2' if plot_type = "ggplot2".

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Anderson et al. 2010 and fitting the default model
+# (note that the model can take a while to fit)
+fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels)
+
+### ggplot2 version of all of the plots can be obtained by adding 'model_type = "ggplot"
+# the forest function creates a forest plot for a fitted RoBMA object, for example,
+# the forest plot for the individual studies and the model-averaged effect size estimate
+forest(fit)
+
+# the conditional effect size estimate
+forest(fit, conditional = TRUE)
+
+# or transforming the effect size estimates to Fisher's z
+forest(fit, output_scale = "fishers_z")
+} # }
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 00000000..01c24914 --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,368 @@ + +Package index • RoBMA + Skip to contents + + +
    +
    +
    + +

    Package index

    +
    + +
    +

    All functions

    + + + + +
    + + + + +
    + + Anderson2010 + +
    +
    27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria
    +
    + + Andrews2021 + +
    +
    36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA
    +
    + + Bem2011 + +
    +
    9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA
    +
    + + BiBMA() + +
    +
    Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data
    +
    + + check_RoBMA() check_RoBMA_convergence() + +
    +
    Check fitted RoBMA object for errors and warnings
    +
    + + check_setup.BiBMA() + +
    +
    Prints summary of "BiBMA.reg" ensemble implied by the specified priors and formula
    +
    + + check_setup() check_setup.RoBMA() + +
    +
    Prints summary of "RoBMA" ensemble implied by the specified priors
    +
    + + check_setup.reg() check_setup.RoBMA.reg() + +
    +
    Prints summary of "RoBMA.reg" ensemble implied by the specified priors and formula
    +
    + + combine_data() + +
    +
    Combines different effect sizes into a common metric
    +
    + + contr.independent() + +
    +
    Independent contrast matrix
    +
    + + contr.meandif() + +
    +
    Mean difference contrast matrix
    +
    + + contr.orthonormal() + +
    +
    Orthornomal contrast matrix
    +
    + + diagnostics() diagnostics_autocorrelation() diagnostics_trace() diagnostics_density() + +
    +
    Checks a fitted RoBMA object
    +
    + + d2r() d2z() d2logOR() d2OR() r2d() r2z() r2logOR() r2OR() z2r() z2d() z2logOR() z2OR() logOR2r() logOR2z() logOR2d() logOR2OR() OR2r() OR2z() OR2logOR() OR2d() + +
    +
    Effect size transformations
    +
    + + forest() + +
    +
    Forest plot for a RoBMA object
    +
    + + interpret() + +
    +
    Interprets results of a RoBMA model.
    +
    + + is.RoBMA() is.RoBMA.reg() is.NoBMA() is.NoBMA.reg() is.BiBMA() + +
    +
    Reports whether x is a RoBMA object
    +
    + + Kroupova2021 + +
    +
    881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA
    +
    + + Lui2015 + +
    +
    18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA
    +
    + + marginal_plot() + +
    +
    Plots marginal estimates of a fitted RoBMA regression object
    +
    + + marginal_summary() + +
    +
    Summarize marginal estimates of a fitted RoBMA regression object
    +
    + + NoBMA() + +
    +
    Estimate a Bayesian Model-Averaged Meta-Analysis
    +
    + + NoBMA.reg() + +
    +
    Estimate a Bayesian Model-Averaged Meta-Regression
    +
    + + plot(<RoBMA>) + +
    +
    Plots a fitted RoBMA object
    +
    + + plot_models() + +
    +
    Models plot for a RoBMA object
    +
    + + Poulsen2006 + +
    +
    5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity
    +
    + + print(<marginal_summary.RoBMA>) + +
    +
    Prints marginal_summary object for RoBMA method
    +
    + + print(<RoBMA>) + +
    +
    Prints a fitted RoBMA object
    +
    + + print(<summary.RoBMA>) + +
    +
    Prints summary object for RoBMA method
    +
    + + prior() + +
    +
    Creates a prior distribution
    +
    + + prior_factor() + +
    +
    Creates a prior distribution for factors
    +
    + + prior_informed() + +
    +
    Creates an informed prior distribution based on research
    +
    + + prior_none() + +
    +
    Creates a prior distribution
    +
    + + prior_PEESE() + +
    +
    Creates a prior distribution for PET or PEESE models
    +
    + + prior_PET() + +
    +
    Creates a prior distribution for PET or PEESE models
    +
    + + prior_weightfunction() + +
    +
    Creates a prior distribution for a weight function
    +
    + + RoBMA-package RoBMA_package RoBMA.package + +
    +
    RoBMA: Robust Bayesian meta-analysis
    +
    + + RoBMA() + +
    +
    Estimate a Robust Bayesian Meta-Analysis
    +
    + + RoBMA.reg() + +
    +
    Estimate a Robust Bayesian Meta-Analysis Meta-Regression
    +
    + + set_autofit_control() set_convergence_checks() + +
    +
    Control MCMC fitting process
    +
    + + RoBMA.options() RoBMA.get_option() + +
    +
    Options for the RoBMA package
    +
    + + se_d() n_d() se_r() n_r() se_z() n_z() + +
    +
    Sample sizes to standard errors calculations
    +
    + + se_d2se_logOR() se_d2se_r() se_r2se_d() se_logOR2se_d() se_d2se_z() se_r2se_z() se_r2se_logOR() se_logOR2se_r() se_logOR2se_z() se_z2se_d() se_z2se_r() se_z2se_logOR() + +
    +
    Standard errors transformations
    +
    + + summary(<RoBMA>) + +
    +
    Summarize fitted RoBMA object
    +
    + + summary_heterogeneity() + +
    +
    Summarizes heterogeneity of a RoBMA model
    +
    + + update(<BiBMA>) + +
    +
    Updates a fitted BiBMA object
    +
    + + update(<RoBMA>) + +
    +
    Updates a fitted RoBMA object
    +
    + + weighted_multivariate_normal + +
    +
    Weighted multivariate normal distribution
    +
    + + dwnorm() pwnorm() qwnorm() rwnorm() + +
    +
    Weighted normal distribution
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/interpret.html b/docs/reference/interpret.html new file mode 100644 index 00000000..b57a538d --- /dev/null +++ b/docs/reference/interpret.html @@ -0,0 +1,95 @@ + +Interprets results of a RoBMA model. — interpret • RoBMA + Skip to contents + + +
    +
    +
    + +

    Interprets results of a RoBMA model.

    + Source: R/summary.R +
    interpret.Rd
    +
    + +
    +

    interpret creates a brief textual summary +of a fitted RoBMA object.

    +
    + +
    +

    Usage

    + 
    interpret(object, output_scale = NULL)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    a fitted RoBMA object

    + + +
    output_scale
    +

    transform the meta-analytic estimates to a different +scale. Defaults to NULL which returns the same scale as the model was estimated on.

    + +
    +
    +

    Value

    +

    interpret returns a character.

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/is.BiBMA.html b/docs/reference/is.BiBMA.html new file mode 100644 index 00000000..abd4a0c4 --- /dev/null +++ b/docs/reference/is.BiBMA.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/is.NoBMA.html b/docs/reference/is.NoBMA.html new file mode 100644 index 00000000..abd4a0c4 --- /dev/null +++ b/docs/reference/is.NoBMA.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/is.NoBMA.reg.html b/docs/reference/is.NoBMA.reg.html new file mode 100644 index 00000000..abd4a0c4 --- /dev/null +++ b/docs/reference/is.NoBMA.reg.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/is.RoBMA.html b/docs/reference/is.RoBMA.html new file mode 100644 index 00000000..10058b78 --- /dev/null +++ b/docs/reference/is.RoBMA.html @@ -0,0 +1,95 @@ + +Reports whether x is a RoBMA object — is.RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Reports whether x is a RoBMA object

    + Source: R/summary.R +
    is.RoBMA.Rd
    +
    + +
    +

    Reports whether x is a RoBMA object

    +
    + +
    +

    Usage

    + 
    is.RoBMA(x)
+
+is.RoBMA.reg(x)
+
+is.NoBMA(x)
+
+is.NoBMA.reg(x)
+
+is.BiBMA(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    an object to test

    + +
    +
    +

    Value

    +

    returns a boolean.

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/is.RoBMA.reg.html b/docs/reference/is.RoBMA.reg.html new file mode 100644 index 00000000..abd4a0c4 --- /dev/null +++ b/docs/reference/is.RoBMA.reg.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/logOR2OR.html b/docs/reference/logOR2OR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/logOR2OR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/logOR2d.html b/docs/reference/logOR2d.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/logOR2d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/logOR2r.html b/docs/reference/logOR2r.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/logOR2r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/logOR2z.html b/docs/reference/logOR2z.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/logOR2z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/marginal_plot.html b/docs/reference/marginal_plot.html new file mode 100644 index 00000000..a3383cf1 --- /dev/null +++ b/docs/reference/marginal_plot.html @@ -0,0 +1,150 @@ + +Plots marginal estimates of a fitted RoBMA regression object — marginal_plot • RoBMA + Skip to contents + + +
    +
    +
    + +

    Plots marginal estimates of a fitted RoBMA regression object

    + Source: R/marginal.R +
    marginal_plot.Rd
    +
    + +
    +

    marginal_plot allows to visualize prior and +posterior distributions of marginal estimates of a RoBMA regression model.

    +
    + +
    +

    Usage

    + 
    marginal_plot(
+  x,
+  parameter,
+  conditional = FALSE,
+  plot_type = "base",
+  prior = FALSE,
+  output_scale = NULL,
+  dots_prior = NULL,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a fitted RoBMA regression object

    + + +
    parameter
    +

    regression parameter to be plotted

    + + +
    conditional
    +

    whether conditional marginal estimates should be +plotted. Defaults to FALSE which plots the model-averaged +estimates.

    + + +
    plot_type
    +

    whether to use a base plot "base" +or ggplot2 "ggplot" for plotting. Defaults to +"base".

    + + +
    prior
    +

    whether prior distribution should be added to +figure. Defaults to FALSE.

    + + +
    output_scale
    +

    transform the effect sizes and the meta-analytic +effect size estimate to a different scale. Defaults to NULL +which returns the same scale as the model was estimated on.

    + + +
    dots_prior
    +

    list of additional graphical arguments +to be passed to the plotting function of the prior +distribution. Supported arguments are lwd, +lty, col, and col.fill, to adjust +the line thickness, line type, line color, and fill color +of the prior distribution respectively.

    + + +
    ...
    +

    list of additional graphical arguments +to be passed to the plotting function. Supported arguments +are lwd, lty, col, col.fill, +xlab, ylab, main, xlim, ylim +to adjust the line thickness, line type, line color, fill color, +x-label, y-label, title, x-axis range, and y-axis range +respectively.

    + +
    +
    +

    Value

    +

    plot.RoBMA returns either NULL if plot_type = "base" +or an object object of class 'ggplot2' if plot_type = "ggplot2".

    +
    +
    +

    See also

    +

    RoBMA()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/marginal_summary.html b/docs/reference/marginal_summary.html new file mode 100644 index 00000000..f89ce553 --- /dev/null +++ b/docs/reference/marginal_summary.html @@ -0,0 +1,125 @@ + +Summarize marginal estimates of a fitted RoBMA regression object — marginal_summary • RoBMA + Skip to contents + + +
    +
    +
    + +

    Summarize marginal estimates of a fitted RoBMA regression object

    + Source: R/marginal.R +
    marginal_summary.Rd
    +
    + +
    +

    marginal_summary creates summary tables for +marginal estimates of a RoBMA regression model.

    +
    + +
    +

    Usage

    + 
    marginal_summary(
+  object,
+  conditional = FALSE,
+  output_scale = NULL,
+  probs = c(0.025, 0.975),
+  logBF = FALSE,
+  BF01 = FALSE
+)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    a fitted RoBMA regression object

    + + +
    conditional
    +

    show the conditional estimates (assuming that the +alternative is true).

    + + +
    output_scale
    +

    transform the meta-analytic estimates to a different +scale. Defaults to NULL which returns the same scale as the model was estimated on.

    + + +
    probs
    +

    quantiles of the posterior samples to be displayed. +Defaults to c(.025, .975)

    + + +
    logBF
    +

    show log of Bayes factors. Defaults to FALSE.

    + + +
    BF01
    +

    show Bayes factors in support of the null hypotheses. Defaults to +FALSE.

    + +
    +
    +

    Value

    +

    marginal_summary returns a list of tables of class 'BayesTools_table'.

    +
    +
    +

    See also

    +

    RoBMA(), summary.RoBMA(), diagnostics(), check_RoBMA()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/n_d.html b/docs/reference/n_d.html new file mode 100644 index 00000000..b4ed7165 --- /dev/null +++ b/docs/reference/n_d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/n_r.html b/docs/reference/n_r.html new file mode 100644 index 00000000..b4ed7165 --- /dev/null +++ b/docs/reference/n_r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/n_z.html b/docs/reference/n_z.html new file mode 100644 index 00000000..b4ed7165 --- /dev/null +++ b/docs/reference/n_z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/plot.RoBMA.html b/docs/reference/plot.RoBMA.html new file mode 100644 index 00000000..1fe21062 --- /dev/null +++ b/docs/reference/plot.RoBMA.html @@ -0,0 +1,205 @@ + +Plots a fitted RoBMA object — plot.RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Plots a fitted RoBMA object

    + Source: R/plots.R +
    plot.RoBMA.Rd
    +
    + +
    +

    plot.RoBMA allows to visualize +different "RoBMA" object parameters in various +ways. See type for the different model types.

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'RoBMA'
+plot(
+  x,
+  parameter = "mu",
+  conditional = FALSE,
+  plot_type = "base",
+  prior = FALSE,
+  output_scale = NULL,
+  rescale_x = FALSE,
+  show_data = TRUE,
+  dots_prior = NULL,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a fitted RoBMA object

    + + +
    parameter
    +

    a parameter to be plotted. Defaults to +"mu" (for the effect size). The additional options +are "tau" (for the heterogeneity), +"weightfunction" (for the estimated weightfunction), +or "PET-PEESE" (for the PET-PEESE regression).

    + + +
    conditional
    +

    whether conditional estimates should be +plotted. Defaults to FALSE which plots the model-averaged +estimates. Note that both "weightfunction" and +"PET-PEESE" are always ignoring the other type of +publication bias adjustment.

    + + +
    plot_type
    +

    whether to use a base plot "base" +or ggplot2 "ggplot" for plotting. Defaults to +"base".

    + + +
    prior
    +

    whether prior distribution should be added to +figure. Defaults to FALSE.

    + + +
    output_scale
    +

    transform the effect sizes and the meta-analytic +effect size estimate to a different scale. Defaults to NULL +which returns the same scale as the model was estimated on.

    + + +
    rescale_x
    +

    whether the x-axis of the "weightfunction" +should be re-scaled to make the x-ticks equally spaced. +Defaults to FALSE.

    + + +
    show_data
    +

    whether the study estimates and standard +errors should be show in the "PET-PEESE" plot. +Defaults to TRUE.

    + + +
    dots_prior
    +

    list of additional graphical arguments +to be passed to the plotting function of the prior +distribution. Supported arguments are lwd, +lty, col, and col.fill, to adjust +the line thickness, line type, line color, and fill color +of the prior distribution respectively.

    + + +
    ...
    +

    list of additional graphical arguments +to be passed to the plotting function. Supported arguments +are lwd, lty, col, col.fill, +xlab, ylab, main, xlim, ylim +to adjust the line thickness, line type, line color, fill color, +x-label, y-label, title, x-axis range, and y-axis range +respectively.

    + +
    +
    +

    Value

    +

    plot.RoBMA returns either NULL if plot_type = "base" +or an object object of class 'ggplot2' if plot_type = "ggplot2".

    +
    +
    +

    See also

    +

    RoBMA()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Anderson et al. 2010 and fitting the default model
+# (note that the model can take a while to fit)
+fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels)
+
+### ggplot2 version of all of the plots can be obtained by adding 'model_type = "ggplot"
+# the 'plot' function allows to visualize the results of a fitted RoBMA object, for example;
+# the model-averaged effect size estimate
+plot(fit, parameter = "mu")
+
+# and show both the prior and posterior distribution
+plot(fit, parameter = "mu", prior = TRUE)
+
+# conditional plots can by obtained by specifying
+plot(fit, parameter = "mu", conditional = TRUE)
+
+# plotting function also allows to visualize the weight function
+plot(fit, parameter = "weightfunction")
+
+# re-scale the x-axis
+plot(fit, parameter = "weightfunction", rescale_x = TRUE)
+
+# or visualize the PET-PEESE regression line
+plot(fit, parameter = "PET-PEESE")
+} # }
+
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/plot_models.html b/docs/reference/plot_models.html new file mode 100644 index 00000000..b677ab64 --- /dev/null +++ b/docs/reference/plot_models.html @@ -0,0 +1,170 @@ + +Models plot for a RoBMA object — plot_models • RoBMA + Skip to contents + + +
    +
    +
    + +

    Models plot for a RoBMA object

    + Source: R/plots.R +
    plot_models.Rd
    +
    + +
    +

    plot_models plots individual models' +estimates for a "RoBMA" object.

    +
    + +
    +

    Usage

    + 
    plot_models(
+  x,
+  parameter = "mu",
+  conditional = FALSE,
+  output_scale = NULL,
+  plot_type = "base",
+  order = "decreasing",
+  order_by = "model",
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a fitted RoBMA object

    + + +
    parameter
    +

    a parameter to be plotted. Defaults to +"mu" (for the effect size). The additional option +is "tau" (for the heterogeneity).

    + + +
    conditional
    +

    whether conditional estimates should be +plotted. Defaults to FALSE which plots the model-averaged +estimates. Note that both "weightfunction" and +"PET-PEESE" are always ignoring the other type of +publication bias adjustment.

    + + +
    output_scale
    +

    transform the effect sizes and the meta-analytic +effect size estimate to a different scale. Defaults to NULL +which returns the same scale as the model was estimated on.

    + + +
    plot_type
    +

    whether to use a base plot "base" +or ggplot2 "ggplot" for plotting. Defaults to +"base".

    + + +
    order
    +

    how the models should be ordered. +Defaults to "decreasing" which orders them in decreasing +order in accordance to order_by argument. The alternative is +"increasing".

    + + +
    order_by
    +

    what feature should be use to order the models. +Defaults to "model" which orders the models according to +their number. The alternatives are "estimate" (for the effect +size estimates), "probability" (for the posterior model probability), +and "BF" (for the inclusion Bayes factor).

    + + +
    ...
    +

    list of additional graphical arguments +to be passed to the plotting function. Supported arguments +are lwd, lty, col, col.fill, +xlab, ylab, main, xlim, ylim +to adjust the line thickness, line type, line color, fill color, +x-label, y-label, title, x-axis range, and y-axis range +respectively.

    + +
    +
    +

    Value

    +

    plot_models returns either NULL if plot_type = "base" +or an object object of class 'ggplot2' if plot_type = "ggplot2".

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Anderson et al. 2010 and fitting the default model
+# (note that the model can take a while to fit)
+fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels)
+
+### ggplot2 version of all of the plots can be obtained by adding 'model_type = "ggplot"
+# the plot_models function creates a plot for of the individual models' estimates, for example,
+# the effect size estimates from the individual models can be obtained with
+plot_models(fit)
+
+# and effect size estimates from only the conditional models
+plot_models(fit, conditional = TRUE)
+} # }
+
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/print.RoBMA.html b/docs/reference/print.RoBMA.html new file mode 100644 index 00000000..38b4d1a2 --- /dev/null +++ b/docs/reference/print.RoBMA.html @@ -0,0 +1,96 @@ + +Prints a fitted RoBMA object — print.RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Prints a fitted RoBMA object

    + Source: R/summary.R +
    print.RoBMA.Rd
    +
    + +
    +

    Prints a fitted RoBMA object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'RoBMA'
+print(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a fitted RoBMA object.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    print.RoBMA invisibly returns the print statement.

    +
    +
    +

    See also

    +

    RoBMA()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/print.marginal_summary.RoBMA.html b/docs/reference/print.marginal_summary.RoBMA.html new file mode 100644 index 00000000..8c883c0d --- /dev/null +++ b/docs/reference/print.marginal_summary.RoBMA.html @@ -0,0 +1,96 @@ + +Prints marginal_summary object for RoBMA method — print.marginal_summary.RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Prints marginal_summary object for RoBMA method

    + Source: R/marginal.R +
    print.marginal_summary.RoBMA.Rd
    +
    + +
    +

    Prints marginal_summary object for RoBMA method

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'marginal_summary.RoBMA'
+print(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a summary of a RoBMA object

    + + +
    ...
    +

    additional arguments

    + +
    +
    +

    Value

    +

    print.marginal_summary.RoBMA invisibly returns the print statement.

    +
    +
    +

    See also

    +

    RoBMA()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/print.summary.RoBMA.html b/docs/reference/print.summary.RoBMA.html new file mode 100644 index 00000000..b3d7b8f2 --- /dev/null +++ b/docs/reference/print.summary.RoBMA.html @@ -0,0 +1,96 @@ + +Prints summary object for RoBMA method — print.summary.RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Prints summary object for RoBMA method

    + Source: R/summary.R +
    print.summary.RoBMA.Rd
    +
    + +
    +

    Prints summary object for RoBMA method

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'summary.RoBMA'
+print(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a summary of a RoBMA object

    + + +
    ...
    +

    additional arguments

    + +
    +
    +

    Value

    +

    print.summary.RoBMA invisibly returns the print statement.

    +
    +
    +

    See also

    +

    RoBMA()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
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    +
    +
    + +

    Creates a prior distribution

    + Source: R/priors.R +
    prior.Rd
    +
    + +
    +

    prior creates a prior distribution. +The prior can be visualized by the plot function.

    +
    + +
    +

    Usage

    + 
    prior(
+  distribution,
+  parameters,
+  truncation = list(lower = -Inf, upper = Inf),
+  prior_weights = 1
+)
    +
    + +
    +

    Arguments

    + + +
    distribution
    +

    name of the prior distribution. The +possible options are

    "point"
    +

    for a point density characterized by a +location parameter.

    + +
    "normal"
    +

    for a normal distribution characterized +by a mean and sd parameters.

    + +
    "lognormal"
    +

    for a lognormal distribution characterized +by a meanlog and sdlog parameters.

    + +
    "cauchy"
    +

    for a Cauchy distribution characterized +by a location and scale parameters. Internally +converted into a generalized t-distribution with df = 1.

    + +
    "t"
    +

    for a generalized t-distribution characterized +by a location, scale, and df parameters.

    + +
    "gamma"
    +

    for a gamma distribution characterized +by either shape and rate, or shape and +scale parameters. The later is internally converted to +the shape and rate parametrization

    + +
    "invgamma"
    +

    for an inverse-gamma distribution +characterized by a shape and scale parameters. The +JAGS part uses a 1/gamma distribution with a shape and rate +parameter.

    + +
    "beta"
    +

    for a beta distribution +characterized by an alpha and beta parameters.

    + +
    "exp"
    +

    for an exponential distribution +characterized by either rate or scale +parameter. The later is internally converted to +rate.

    + +
    "uniform"
    +

    for a uniform distribution defined on a +range from a to b

    + + +
    + + +
    parameters
    +

    list of appropriate parameters for a given +distribution.

    + + +
    truncation
    +

    list with two elements, lower and +upper, that define the lower and upper truncation of the +distribution. Defaults to list(lower = -Inf, upper = Inf). +The truncation is automatically set to the bounds of the support.

    + + +
    prior_weights
    +

    prior odds associated with a given distribution. +The value is passed into the model fitting function, which creates models +corresponding to all combinations of prior distributions for each of +the model parameters and sets the model priors odds to the product +of its prior distributions.

    + +
    +
    +

    Value

    +

    prior and prior_none return an object of class 'prior'. +A named list containing the distribution name, parameters, and prior weights.

    +
    +
    +

    See also

    +

    plot.prior(), Normal, Lognormal, Cauchy, +Beta, Exponential, +LocationScaleT, InvGamma.

    +
    + +
    +

    Examples

    + 
    # create a standard normal prior distribution
+p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))
+
+# create a half-normal standard normal prior distribution
+p2 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1),
+truncation = list(lower = 0, upper = Inf))
+
+# the prior distribution can be visualized using the plot function
+# (see ?plot.prior for all options)
+plot(p1)
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
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zbAfcG!|9M-O|_P)sE}FWQuS>Io-Earau0Rdv%k{%xpYCslgiLJ4>ddOZftFug`f7) zdFxXn?k07{wn$j_I%Ahk#P#;GPdgQDS2r!f+2e)YiZv}^Je7%hX{w!FiS@%Ny>)eN$9d;Gu8KOS4NLuJA%VfNX=5OW)`=%!bIYYye7;K#=KuZYUpr7XC=n65jq|#Vp+5Pi+S~NDCT_7j_dfsx Cy|?rL literal 0 HcmV?d00001 diff --git a/docs/reference/prior_PEESE.html b/docs/reference/prior_PEESE.html new file mode 100644 index 00000000..cf3112a3 --- /dev/null +++ b/docs/reference/prior_PEESE.html @@ -0,0 +1,181 @@ + +Creates a prior distribution for PET or PEESE models — prior_PEESE • RoBMA + Skip to contents + + +

    +
    +
    + +

    Creates a prior distribution for PET or PEESE models

    + Source: R/priors.R +
    prior_PEESE.Rd
    +
    + +
    +

    prior creates a prior distribution for fitting a PET or +PEESE style models in RoBMA. The prior distribution can be visualized +by the plot function.

    +
    + +
    +

    Usage

    + 
    prior_PEESE(
+  distribution,
+  parameters,
+  truncation = list(lower = 0, upper = Inf),
+  prior_weights = 1
+)
    +
    + +
    +

    Arguments

    + + +
    distribution
    +

    name of the prior distribution. The +possible options are

    "point"
    +

    for a point density characterized by a +location parameter.

    + +
    "normal"
    +

    for a normal distribution characterized +by a mean and sd parameters.

    + +
    "lognormal"
    +

    for a lognormal distribution characterized +by a meanlog and sdlog parameters.

    + +
    "cauchy"
    +

    for a Cauchy distribution characterized +by a location and scale parameters. Internally +converted into a generalized t-distribution with df = 1.

    + +
    "t"
    +

    for a generalized t-distribution characterized +by a location, scale, and df parameters.

    + +
    "gamma"
    +

    for a gamma distribution characterized +by either shape and rate, or shape and +scale parameters. The later is internally converted to +the shape and rate parametrization

    + +
    "invgamma"
    +

    for an inverse-gamma distribution +characterized by a shape and scale parameters. The +JAGS part uses a 1/gamma distribution with a shape and rate +parameter.

    + +
    "beta"
    +

    for a beta distribution +characterized by an alpha and beta parameters.

    + +
    "exp"
    +

    for an exponential distribution +characterized by either rate or scale +parameter. The later is internally converted to +rate.

    + +
    "uniform"
    +

    for a uniform distribution defined on a +range from a to b

    + + +
    + + +
    parameters
    +

    list of appropriate parameters for a given +distribution.

    + + +
    truncation
    +

    list with two elements, lower and +upper, that define the lower and upper truncation of the +distribution. Defaults to list(lower = -Inf, upper = Inf). +The truncation is automatically set to the bounds of the support.

    + + +
    prior_weights
    +

    prior odds associated with a given distribution. +The value is passed into the model fitting function, which creates models +corresponding to all combinations of prior distributions for each of +the model parameters and sets the model priors odds to the product +of its prior distributions.

    + +
    +
    +

    Value

    +

    prior_PET and prior_PEESE return an object of class 'prior'.

    +
    +
    +

    See also

    +

    plot.prior(), prior()

    +
    + +
    +

    Examples

    + 
    # create a half-Cauchy prior distribution
+# (PET and PEESE specific functions automatically set lower truncation at 0)
+p1 <- prior_PET(distribution = "Cauchy", parameters = list(location = 0, scale = 1))
+
+plot(p1)
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
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zbAfcG!|9M-O|_P)sE}FWQuS>Io-Earau0Rdv%k{%xpYCslgiLJ4>ddOZftFug`f7) zdFxXn?k07{wn$j_I%Ahk#P#;GPdgQDS2r!f+2e)YiZv}^Je7%hX{w!FiS@%Ny>)eN$9d;Gu8KOS4NLuJA%VfNX=5OW)`=%!bIYYye7;K#=KuZYUpr7XC=n65jq|#Vp+5Pi+S~NDCT_7j_dfsx Cy|?rL literal 0 HcmV?d00001 diff --git a/docs/reference/prior_PET.html b/docs/reference/prior_PET.html new file mode 100644 index 00000000..71dec315 --- /dev/null +++ b/docs/reference/prior_PET.html @@ -0,0 +1,181 @@ + +Creates a prior distribution for PET or PEESE models — prior_PET • RoBMA + Skip to contents + + +

    +
    +
    + +

    Creates a prior distribution for PET or PEESE models

    + Source: R/priors.R +
    prior_PET.Rd
    +
    + +
    +

    prior creates a prior distribution for fitting a PET or +PEESE style models in RoBMA. The prior distribution can be visualized +by the plot function.

    +
    + +
    +

    Usage

    + 
    prior_PET(
+  distribution,
+  parameters,
+  truncation = list(lower = 0, upper = Inf),
+  prior_weights = 1
+)
    +
    + +
    +

    Arguments

    + + +
    distribution
    +

    name of the prior distribution. The +possible options are

    "point"
    +

    for a point density characterized by a +location parameter.

    + +
    "normal"
    +

    for a normal distribution characterized +by a mean and sd parameters.

    + +
    "lognormal"
    +

    for a lognormal distribution characterized +by a meanlog and sdlog parameters.

    + +
    "cauchy"
    +

    for a Cauchy distribution characterized +by a location and scale parameters. Internally +converted into a generalized t-distribution with df = 1.

    + +
    "t"
    +

    for a generalized t-distribution characterized +by a location, scale, and df parameters.

    + +
    "gamma"
    +

    for a gamma distribution characterized +by either shape and rate, or shape and +scale parameters. The later is internally converted to +the shape and rate parametrization

    + +
    "invgamma"
    +

    for an inverse-gamma distribution +characterized by a shape and scale parameters. The +JAGS part uses a 1/gamma distribution with a shape and rate +parameter.

    + +
    "beta"
    +

    for a beta distribution +characterized by an alpha and beta parameters.

    + +
    "exp"
    +

    for an exponential distribution +characterized by either rate or scale +parameter. The later is internally converted to +rate.

    + +
    "uniform"
    +

    for a uniform distribution defined on a +range from a to b

    + + +
    + + +
    parameters
    +

    list of appropriate parameters for a given +distribution.

    + + +
    truncation
    +

    list with two elements, lower and +upper, that define the lower and upper truncation of the +distribution. Defaults to list(lower = -Inf, upper = Inf). +The truncation is automatically set to the bounds of the support.

    + + +
    prior_weights
    +

    prior odds associated with a given distribution. +The value is passed into the model fitting function, which creates models +corresponding to all combinations of prior distributions for each of +the model parameters and sets the model priors odds to the product +of its prior distributions.

    + +
    +
    +

    Value

    +

    prior_PET and prior_PEESE return an object of class 'prior'.

    +
    +
    +

    See also

    +

    plot.prior(), prior()

    +
    + +
    +

    Examples

    + 
    # create a half-Cauchy prior distribution
+# (PET and PEESE specific functions automatically set lower truncation at 0)
+p1 <- prior_PET(distribution = "Cauchy", parameters = list(location = 0, scale = 1))
+
+plot(p1)
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/prior_factor.html b/docs/reference/prior_factor.html new file mode 100644 index 00000000..2d338ce7 --- /dev/null +++ b/docs/reference/prior_factor.html @@ -0,0 +1,210 @@ + +Creates a prior distribution for factors — prior_factor • RoBMA + Skip to contents + + +
    +
    +
    + +

    Creates a prior distribution for factors

    + Source: R/priors.R +
    prior_factor.Rd
    +
    + +
    +

    prior_factor creates a prior distribution for fitting +models with factor predictors. (Note that results across different operating +systems might vary due to differences in JAGS numerical precision.)

    +
    + +
    +

    Usage

    + 
    prior_factor(
+  distribution,
+  parameters,
+  truncation = list(lower = -Inf, upper = Inf),
+  prior_weights = 1,
+  contrast = "meandif"
+)
    +
    + +
    +

    Arguments

    + + +
    distribution
    +

    name of the prior distribution. The +possible options are

    "point"
    +

    for a point density characterized by a +location parameter.

    + +
    "normal"
    +

    for a normal distribution characterized +by a mean and sd parameters.

    + +
    "lognormal"
    +

    for a lognormal distribution characterized +by a meanlog and sdlog parameters.

    + +
    "cauchy"
    +

    for a Cauchy distribution characterized +by a location and scale parameters. Internally +converted into a generalized t-distribution with df = 1.

    + +
    "t"
    +

    for a generalized t-distribution characterized +by a location, scale, and df parameters.

    + +
    "gamma"
    +

    for a gamma distribution characterized +by either shape and rate, or shape and +scale parameters. The later is internally converted to +the shape and rate parametrization

    + +
    "invgamma"
    +

    for an inverse-gamma distribution +characterized by a shape and scale parameters. The +JAGS part uses a 1/gamma distribution with a shape and rate +parameter.

    + +
    "beta"
    +

    for a beta distribution +characterized by an alpha and beta parameters.

    + +
    "exp"
    +

    for an exponential distribution +characterized by either rate or scale +parameter. The later is internally converted to +rate.

    + +
    "uniform"
    +

    for a uniform distribution defined on a +range from a to b

    + + +
    + + +
    parameters
    +

    list of appropriate parameters for a given +distribution.

    + + +
    truncation
    +

    list with two elements, lower and +upper, that define the lower and upper truncation of the +distribution. Defaults to list(lower = -Inf, upper = Inf). +The truncation is automatically set to the bounds of the support.

    + + +
    prior_weights
    +

    prior odds associated with a given distribution. +The value is passed into the model fitting function, which creates models +corresponding to all combinations of prior distributions for each of +the model parameters and sets the model priors odds to the product +of its prior distributions.

    + + +
    contrast
    +

    type of contrast for the prior distribution. The possible options are

    "meandif"
    +

    for contrast centered around the grand mean +with equal marginal distributions, making the prior distribution exchangeable +across factor levels. In contrast to "orthonormal", the marginal distributions +are identical regardless of the number of factor levels and the specified prior +distribution corresponds to the difference from grand mean for each factor level. +Only supports distribution = "mnormal" and distribution = "mt" +which generates the corresponding multivariate normal/t distributions.

    + +
    "orthonormal"
    +

    for contrast centered around the grand mean +with equal marginal distributions, making the prior distribution exchangeable +across factor levels. Only supports distribution = "mnormal" and +distribution = "mt" which generates the corresponding multivariate normal/t +distributions.

    + +
    "treatment"
    +

    for contrasts using the first level as a comparison +group and setting equal prior distribution on differences between the individual +factor levels and the comparison level.

    + +
    "independent"
    +

    for contrasts specifying dependent prior distribution +for each factor level (note that this leads to an overparameterized model if the +intercept is included).

    + + +
    + +
    +
    +

    Value

    +

    return an object of class 'prior'.

    +
    +
    +

    See also

    +

    prior()

    +
    + +
    +

    Examples

    + 
    # create an orthonormal prior distribution
+p1 <- prior_factor(distribution = "mnormal", contrast = "orthonormal",
+                   parameters = list(mean = 0, sd = 1))
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
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b/docs/reference/prior_informed.html new file mode 100644 index 00000000..c9f823f0 --- /dev/null +++ b/docs/reference/prior_informed.html @@ -0,0 +1,172 @@ + +Creates an informed prior distribution based on research — prior_informed • RoBMA + Skip to contents + + +

    +
    +
    + +

    Creates an informed prior distribution based on research

    + Source: R/priors.R +
    prior_informed.Rd
    +
    + +
    +

    prior_informed creates an informed prior distribution based on past +research. The prior can be visualized by the plot function.

    +
    + +
    +

    Usage

    + 
    prior_informed(name, parameter = NULL, type = "smd")
    +
    + +
    +

    Arguments

    + + +
    name
    +

    name of the prior distribution. There are many options based on prior psychological +or medical research. +For psychology, the possible options are

    "van Erp"
    +

    for an informed prior distribution for the heterogeneity parameter tau +of meta-analytic effect size estimates based on standardized mean differences +(van Erp et al. 2017),

    + +
    "Oosterwijk"
    +

    for an informed prior distribution for the effect sizes expected in +social psychology based on prior elicitation with dr. Oosterwijk +(Gronau et al. 2017).

    + + +

    For medicine, the possible options are based on Bartoš et al. (2021) +and Bartoš et al. (2023) +who developed empirical prior distributions for the effect size and heterogeneity parameters of the +continuous outcomes (standardized mean differences), dichotomous outcomes (logOR, logRR, and risk differences), +and time to event outcomes (logHR) based on the Cochrane database of systematic reviews. +Use "Cochrane" for a prior distribution based on the whole database or call +print(prior_informed_medicine_names) to inspect the names of +all 46 subfields and set the appropriate parameter and type.

    + + +
    parameter
    +

    parameter name describing what prior distribution is supposed to be produced in cases +where the name corresponds to multiple prior distributions. Relevant only for the empirical medical +prior distributions.

    + + +
    type
    +

    prior type describing what prior distribution is supposed to be produced in cases +where the name and parameter correspond to multiple prior distributions. Relevant only for +the empirical medical prior distributions with the following options

    "smd"
    +

    for standardized mean differences

    + +
    "logOR"
    +

    for log odds ratios

    + +
    "logRR"
    +

    for log risk ratios

    + +
    "RD"
    +

    for risk differences

    + +
    "logHR"
    +

    for hazard ratios

    + + +
    + +
    +
    +

    Value

    +

    prior_informed returns an object of class 'prior'.

    +
    +
    +

    Details

    +

    Further details can be found in erp2017estimates;textualRoBMA, +gronau2017bayesian;textualRoBMA, and +bartos2021bayesian;textualRoBMA.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    prior(), prior_informed_medicine_names

    +
    + +
    +

    Examples

    + 
    # prior distribution representing expected effect sizes in social psychology
+# based on prior elicitation with dr. Oosterwijk
+p1 <- prior_informed("Oosterwijk")
+
+# the prior distribution can be visualized using the plot function
+# (see ?plot.prior for all options)
+plot(p1)
+
+
+
+# empirical prior distribution for the standardized mean differences from the oral health
+# medical subfield based on meta-analytic effect size estimates from the
+# Cochrane database of systematic reviews
+p2 <- prior_informed("Oral Health", parameter ="effect", type ="smd")
+print(p2)
+#> Student-t(0, 0.51, 5)
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
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    +
    +
    + +

    Creates a prior distribution

    + Source: R/priors.R +
    prior_none.Rd
    +
    + +
    +

    prior creates a prior distribution. +The prior can be visualized by the plot function.

    +
    + +
    +

    Usage

    + 
    prior_none(prior_weights = 1)
    +
    + +
    +

    Arguments

    + + +
    prior_weights
    +

    prior odds associated with a given distribution. +The value is passed into the model fitting function, which creates models +corresponding to all combinations of prior distributions for each of +the model parameters and sets the model priors odds to the product +of its prior distributions.

    + +
    +
    +

    Value

    +

    prior and prior_none return an object of class 'prior'. +A named list containing the distribution name, parameters, and prior weights.

    +
    +
    +

    See also

    +

    plot.prior(), Normal, Lognormal, Cauchy, +Beta, Exponential, +LocationScaleT, InvGamma.

    +
    + +
    +

    Examples

    + 
    # create a standard normal prior distribution
+p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))
+
+# create a half-normal standard normal prior distribution
+p2 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1),
+truncation = list(lower = 0, upper = Inf))
+
+# the prior distribution can be visualized using the plot function
+# (see ?plot.prior for all options)
+plot(p1)
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
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zSNE}g2hS88uzH*QtzrG_RolURfg6E27lCo>R}Sj+{y*<(237>tI!Zu7z~J+L=2X3Z U+vlyRo(K~0boFyt=akR{08Z2dk^lez literal 0 HcmV?d00001 diff --git a/docs/reference/prior_weightfunction.html b/docs/reference/prior_weightfunction.html new file mode 100644 index 00000000..133b839f --- /dev/null +++ b/docs/reference/prior_weightfunction.html @@ -0,0 +1,137 @@ + +Creates a prior distribution for a weight function — prior_weightfunction • RoBMA + Skip to contents + + +
    +
    +
    + +

    Creates a prior distribution for a weight function

    + Source: R/priors.R +
    prior_weightfunction.Rd
    +
    + +
    +

    prior_weightfunction creates a prior distribution for fitting +a RoBMA selection model. The prior can be visualized by the plot function.

    +
    + +
    +

    Usage

    + 
    prior_weightfunction(distribution, parameters, prior_weights = 1)
    +
    + +
    +

    Arguments

    + + +
    distribution
    +

    name of the prior distribution. The +possible options are

    "two.sided"
    +

    for a two-sided weight function +characterized by a vector steps and vector alpha +parameters. The alpha parameter determines an alpha +parameter of Dirichlet distribution which cumulative sum +is used for the weights omega.

    + +
    "one.sided"
    +

    for a one-sided weight function +characterized by either a vector steps and vector +alpha parameter, leading to a monotonic one-sided +function, or by a vector steps, vector alpha1, +and vector alpha2 parameters leading non-monotonic +one-sided weight function. The alpha / alpha1 and +alpha2 parameters determine an alpha parameter of +Dirichlet distribution which cumulative sum is used for +the weights omega.

    + + +
    + + +
    parameters
    +

    list of appropriate parameters for a given +distribution.

    + + +
    prior_weights
    +

    prior odds associated with a given distribution. +The model fitting function usually creates models corresponding to +all combinations of prior distributions for each of the model +parameters, and sets the model priors odds to the product of +its prior distributions.

    + +
    +
    +

    Value

    +

    prior_weightfunction returns an object of class 'prior'.

    +
    +
    +

    See also

    +

    plot.prior()

    +
    + +
    +

    Examples

    + 
    p1 <- prior_weightfunction("one-sided", parameters = list(steps = c(.05, .10), alpha = c(1, 1, 1)))
+
+# the prior distribution can be visualized using the plot function
+# (see ?plot.prior for all options)
+plot(p1)
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/pwnorm.html b/docs/reference/pwnorm.html new file mode 100644 index 00000000..7115b8cc --- /dev/null +++ b/docs/reference/pwnorm.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/qwnorm.html b/docs/reference/qwnorm.html new file mode 100644 index 00000000..7115b8cc --- /dev/null +++ b/docs/reference/qwnorm.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/r2OR.html b/docs/reference/r2OR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/r2OR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/r2d.html b/docs/reference/r2d.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/r2d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/r2logOR.html b/docs/reference/r2logOR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/r2logOR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/r2z.html b/docs/reference/r2z.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/r2z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/rwnorm.html b/docs/reference/rwnorm.html new file mode 100644 index 00000000..7115b8cc --- /dev/null +++ b/docs/reference/rwnorm.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/sample_sizes.html b/docs/reference/sample_sizes.html new file mode 100644 index 00000000..3bb2929c --- /dev/null +++ b/docs/reference/sample_sizes.html @@ -0,0 +1,133 @@ + +Sample sizes to standard errors calculations — sample_sizes • RoBMA + Skip to contents + + +
    +
    +
    + +

    Sample sizes to standard errors calculations

    + Source: R/transformations.R +
    sample_sizes.Rd
    +
    + +
    +

    Functions for transforming between standard +errors and sample sizes (assuming equal sample sizes per group).

    +
    + +
    +

    Usage

    + 
    se_d(d, n)
+
+n_d(d, se)
+
+se_r(r, n)
+
+n_r(r, se)
+
+se_z(n)
+
+n_z(se)
    +
    + +
    +

    Arguments

    + + +
    d
    +

    Cohen's d

    + + +
    n
    +

    sample size of the corresponding effect size

    + + +
    se
    +

    standard error of the corresponding effect size

    + + +
    r
    +

    correlation coefficient

    + +
    +
    +

    Details

    +

    Calculations for Cohen's d, Fisher's z, and log(OR) are +based on borenstein2011introductionRoBMA. Calculations +for correlation coefficient were modified to make the standard error +corresponding to the computed on Fisher's z scale under the same sample +size (in order to make all other transformations consistent). In case that +a direct transformation is not available, the transformations +are chained to provide the effect size of interest.

    +

    Note that sample size and standard error calculation for log(OR) +is not available. The standard error is highly dependent on the +odds within the groups and sample sizes for individual events are +required. Theoretically, the sample size could be obtained by +transforming the effect size and standard error to a different measure +and obtaining the sample size using corresponding function, however, +it leads to a very poor approximation and it is not recommended.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    effect_sizes(), standard_errors()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/se_d.html b/docs/reference/se_d.html new file mode 100644 index 00000000..b4ed7165 --- /dev/null +++ b/docs/reference/se_d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_d2se_logOR.html b/docs/reference/se_d2se_logOR.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_d2se_logOR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_d2se_r.html b/docs/reference/se_d2se_r.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_d2se_r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_d2se_z.html b/docs/reference/se_d2se_z.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_d2se_z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_logOR2se_d.html b/docs/reference/se_logOR2se_d.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_logOR2se_d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_logOR2se_r.html b/docs/reference/se_logOR2se_r.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_logOR2se_r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_logOR2se_z.html b/docs/reference/se_logOR2se_z.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_logOR2se_z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_r.html b/docs/reference/se_r.html new file mode 100644 index 00000000..b4ed7165 --- /dev/null +++ b/docs/reference/se_r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_r2se_d.html b/docs/reference/se_r2se_d.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_r2se_d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_r2se_logOR.html b/docs/reference/se_r2se_logOR.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_r2se_logOR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_r2se_z.html b/docs/reference/se_r2se_z.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_r2se_z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_z.html b/docs/reference/se_z.html new file mode 100644 index 00000000..b4ed7165 --- /dev/null +++ b/docs/reference/se_z.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_z2se_d.html b/docs/reference/se_z2se_d.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_z2se_d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_z2se_logOR.html b/docs/reference/se_z2se_logOR.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_z2se_logOR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/se_z2se_r.html b/docs/reference/se_z2se_r.html new file mode 100644 index 00000000..5fade7ee --- /dev/null +++ b/docs/reference/se_z2se_r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/set_autofit_control,.html b/docs/reference/set_autofit_control,.html new file mode 100644 index 00000000..49263ba2 --- /dev/null +++ b/docs/reference/set_autofit_control,.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/set_autofit_control.html b/docs/reference/set_autofit_control.html new file mode 100644 index 00000000..49263ba2 --- /dev/null +++ b/docs/reference/set_autofit_control.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/set_convergence_checks.html b/docs/reference/set_convergence_checks.html new file mode 100644 index 00000000..49263ba2 --- /dev/null +++ b/docs/reference/set_convergence_checks.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/standard_errors.html b/docs/reference/standard_errors.html new file mode 100644 index 00000000..5faa24d8 --- /dev/null +++ b/docs/reference/standard_errors.html @@ -0,0 +1,169 @@ + +Standard errors transformations — standard_errors • RoBMA + Skip to contents + + +
    +
    +
    + +

    Standard errors transformations

    + Source: R/transformations.R +
    standard_errors.Rd
    +
    + +
    +

    Functions for transforming between +standard errors of different effect size measures.

    +
    + +
    +

    Usage

    + 
    se_d2se_logOR(se_d, logOR)
+
+se_d2se_r(se_d, d)
+
+se_r2se_d(se_r, r)
+
+se_logOR2se_d(se_logOR, logOR)
+
+se_d2se_z(se_d, d)
+
+se_r2se_z(se_r, r)
+
+se_r2se_logOR(se_r, r)
+
+se_logOR2se_r(se_logOR, logOR)
+
+se_logOR2se_z(se_logOR, logOR)
+
+se_z2se_d(se_z, z)
+
+se_z2se_r(se_z, z)
+
+se_z2se_logOR(se_z, z)
    +
    + +
    +

    Arguments

    + + +
    se_d
    +

    standard error of Cohen's d

    + + +
    logOR
    +

    log(odds ratios)

    + + +
    d
    +

    Cohen's d

    + + +
    se_r
    +

    standard error of correlation coefficient

    + + +
    r
    +

    correlation coefficient

    + + +
    se_logOR
    +

    standard error of log(odds ratios)

    + + +
    se_z
    +

    standard error of Fisher's z

    + + +
    z
    +

    Fisher's z

    + +
    +
    +

    Details

    +

    Transformations for Cohen's d, Fisher's z, and log(OR) are +based on borenstein2011introductionRoBMA. Calculations +for correlation coefficient were modified to make the standard error +corresponding to the computed on Fisher's z scale under the same sample +size (in order to make all other transformations consistent). In case that +a direct transformation is not available, the transformations +are chained to provide the effect size of interest.

    +

    It is important to keep in mind that the transformations are only +approximations to the true values. From our experience, +se_d2se_z works well for values of se(Cohen's d) < 0.5. Do +not forget that the effect sizes are standardized and variance of +Cohen's d = 1. Therefore, a standard error of study cannot be larger +unless the participants provided negative information (of course, the +variance is dependent on the effect size as well, and, can therefore be +larger).

    +

    When setting prior distributions, do NOT attempt to transform a standard +normal distribution on Cohen's d (mean = 0, sd = 1) to a normal +distribution on Fisher's z with mean 0 and sd = se_d2se_z(0, 1). +The approximation does NOT work well in this range of values. Instead, +approximate the sd of distribution on Fisher's z using samples in this way: +sd(d2z(rnorm(10000, 0, 1))) or, specify the distribution on Cohen's d +directly.

    +
    +
    +

    References

    +

    +
    +
    +

    See also

    +

    effect_sizes(), sample_sizes()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/summary.RoBMA.html b/docs/reference/summary.RoBMA.html new file mode 100644 index 00000000..06840676 --- /dev/null +++ b/docs/reference/summary.RoBMA.html @@ -0,0 +1,183 @@ + +Summarize fitted RoBMA object — summary.RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Summarize fitted RoBMA object

    + Source: R/summary.R +
    summary.RoBMA.Rd
    +
    + +
    +

    summary.RoBMA creates summary tables for a +RoBMA object.

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'RoBMA'
+summary(
+  object,
+  type = "ensemble",
+  conditional = FALSE,
+  output_scale = NULL,
+  probs = c(0.025, 0.975),
+  logBF = FALSE,
+  BF01 = FALSE,
+  short_name = FALSE,
+  remove_spike_0 = FALSE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    a fitted RoBMA object

    + + +
    type
    +

    whether to show the overall RoBMA results ("ensemble"), +an overview of the individual models ("models"), an overview of +the individual models MCMC diagnostics ("diagnostics"), or a detailed summary +of the individual models ("individual"). Can be abbreviated to first letters.

    + + +
    conditional
    +

    show the conditional estimates (assuming that the +alternative is true). Defaults to FALSE. Only available for +type == "ensemble".

    + + +
    output_scale
    +

    transform the meta-analytic estimates to a different +scale. Defaults to NULL which returns the same scale as the model was estimated on.

    + + +
    probs
    +

    quantiles of the posterior samples to be displayed. +Defaults to c(.025, .975)

    + + +
    logBF
    +

    show log of Bayes factors. Defaults to FALSE.

    + + +
    BF01
    +

    show Bayes factors in support of the null hypotheses. Defaults to +FALSE.

    + + +
    short_name
    +

    whether priors names should be shortened to the first +(couple) of letters. Defaults to FALSE.

    + + +
    remove_spike_0
    +

    whether spike prior distributions with location at zero should +be omitted from the summary. Defaults to FALSE.

    + + +
    ...
    +

    additional arguments

    + +
    +
    +

    Value

    +

    summary.RoBMA returns a list of tables of class 'BayesTools_table'.

    +
    +
    +

    Note

    +

    See diagnostics() for visual convergence checks of the individual models.

    +
    +
    +

    See also

    +

    RoBMA(), diagnostics(), check_RoBMA()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Anderson et al. 2010 and fitting the default model
+# (note that the model can take a while to fit)
+fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels)
+
+# summary can provide many details about the model
+summary(fit)
+
+# estimates from the conditional models can be obtained with
+summary(fit, conditional = TRUE)
+
+# overview of the models and their prior and posterior probability, marginal likelihood,
+# and inclusion Bayes factor can be obtained with
+summary(fit, type = "models")
+
+# diagnostics overview, containing the maximum R-hat, minimum ESS, maximum MCMC error, and
+# maximum MCMC error / sd across parameters for each individual model can be obtained with
+summary(fit, type = "diagnostics")
+
+# summary of individual models and their parameters can be further obtained by
+summary(fit, type = "individual")
+} # }
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/summary_heterogeneity.html b/docs/reference/summary_heterogeneity.html new file mode 100644 index 00000000..98bde763 --- /dev/null +++ b/docs/reference/summary_heterogeneity.html @@ -0,0 +1,133 @@ + +Summarizes heterogeneity of a RoBMA model — summary_heterogeneity • RoBMA + Skip to contents + + +
    +
    +
    + +

    Summarizes heterogeneity of a RoBMA model

    + Source: R/summary-heterogeneity.R +
    summary_heterogeneity.Rd
    +
    + +
    +

    Computes the prediction interval, the absolute +heterogeneity (tau, tau^2), and relative measures of heterogeneity +(I^2, H^2) for a fitted RoBMA object.

    +
    + +
    +

    Usage

    + 
    summary_heterogeneity(
+  object,
+  type = "ensemble",
+  conditional = FALSE,
+  output_scale = NULL,
+  probs = c(0.025, 0.975),
+  short_name = FALSE,
+  remove_spike_0 = FALSE
+)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    a fitted RoBMA object

    + + +
    type
    +

    whether to show the overall RoBMA results ("ensemble") +or a detailed summary of the individual models ("individual"). +Can be abbreviated to first letters.

    + + +
    conditional
    +

    show the conditional estimates (assuming that the +alternative is true). Defaults to FALSE. Only available for +type == "ensemble".

    + + +
    output_scale
    +

    transform the meta-analytic estimates to a different +scale. Defaults to NULL which returns the same scale as the model was estimated on.

    + + +
    probs
    +

    quantiles of the posterior samples to be displayed. +Defaults to c(.025, .975)

    + + +
    short_name
    +

    whether priors names should be shortened to the first +(couple) of letters. Defaults to FALSE.

    + + +
    remove_spike_0
    +

    whether spike prior distributions with location at zero should +be omitted from the summary. Defaults to FALSE.

    + +
    +
    +

    Value

    +

    summary.RoBMA returns a list of tables of class 'BayesTools_table'.

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/update.BiBMA.html b/docs/reference/update.BiBMA.html new file mode 100644 index 00000000..da76395e --- /dev/null +++ b/docs/reference/update.BiBMA.html @@ -0,0 +1,283 @@ + +Updates a fitted BiBMA object — update.BiBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Updates a fitted BiBMA object

    + Source: R/BiBMA.R +
    update.BiBMA.Rd
    +
    + +
    +

    update.BiBMA can be used to

    1. add an additional model to an existing "BiBMA" object by +specifying either a null or alternative prior for each parameter +and the prior odds of the model (prior_weights), see the +vignette("CustomEnsembles") vignette,

      2. +

    2. change the prior odds of fitted models by specifying a vector +prior_weights of the same length as the fitted models,

      3. +

    3. refitting models that failed to converge with updated settings +of control parameters,

      4. +

    4. or changing the convergence criteria and recalculating the ensemble +results by specifying new control argument and setting +refit_failed == FALSE.

      5. +
    + +
    +

    Usage

    + 
    # S3 method for class 'BiBMA'
+update(
+  object,
+  refit_failed = TRUE,
+  extend_all = FALSE,
+  prior_effect = NULL,
+  prior_heterogeneity = NULL,
+  prior_baseline = NULL,
+  prior_weights = NULL,
+  prior_effect_null = NULL,
+  prior_heterogeneity_null = NULL,
+  prior_baseline_null = NULL,
+  study_names = NULL,
+  chains = NULL,
+  adapt = NULL,
+  burnin = NULL,
+  sample = NULL,
+  thin = NULL,
+  autofit = NULL,
+  parallel = NULL,
+  autofit_control = NULL,
+  convergence_checks = NULL,
+  save = "all",
+  seed = NULL,
+  silent = TRUE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    a fitted BiBMA object

    + + +
    refit_failed
    +

    whether failed models should be refitted. Relevant only +if new priors or prior_weights are not supplied. Defaults to TRUE.

    + + +
    extend_all
    +

    extend sampling in all fitted models based on "sample_extend" +argument in set_autofit_control() function. Defaults to FALSE.

    + + +
    prior_effect
    +

    prior distribution for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. +Defaults to NULL.

    + + +
    prior_heterogeneity
    +

    prior distribution for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. +Defaults to NULL.

    + + +
    prior_baseline
    +

    prior distribution for the intercepts (pi) of each study +that will be treated as belonging to the alternative hypothesis. Defaults to NULL.

    + + +
    prior_weights
    +

    either a single value specifying prior model weight +of a newly specified model using priors argument, or a vector of the +same length as already fitted models to update their prior weights.

    + + +
    prior_effect_null
    +

    prior distribution for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. +Defaults to NULL.

    + + +
    prior_heterogeneity_null
    +

    prior distribution for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. +Defaults to NULL.

    + + +
    prior_baseline_null
    +

    prior distribution for the intercepts (pi) of each study +that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    chains
    +

    a number of chains of the MCMC algorithm.

    + + +
    adapt
    +

    a number of adaptation iterations of the MCMC algorithm. +Defaults to 500.

    + + +
    burnin
    +

    a number of burnin iterations of the MCMC algorithm. +Defaults to 2000.

    + + +
    sample
    +

    a number of sampling iterations of the MCMC algorithm. +Defaults to 5000.

    + + +
    thin
    +

    a thinning of the chains of the MCMC algorithm. Defaults to +1.

    + + +
    autofit
    +

    whether the model should be fitted until the convergence +criteria (specified in autofit_control) are satisfied. Defaults to +TRUE.

    + + +
    parallel
    +

    whether the individual models should be fitted in parallel. +Defaults to FALSE. The implementation is not completely stable +and might cause a connection error.

    + + +
    autofit_control
    +

    allows to pass autofit control settings with the +set_autofit_control() function. See ?set_autofit_control for +options and default settings.

    + + +
    convergence_checks
    +

    automatic convergence checks to assess the fitted +models, passed with set_convergence_checks() function. See +?set_convergence_checks for options and default settings.

    + + +
    save
    +

    whether all models posterior distributions should be kept +after obtaining a model-averaged result. Defaults to "all" which +does not remove anything. Set to "min" to significantly reduce +the size of final object, however, some model diagnostics and further +manipulation with the object will not be possible.

    + + +
    seed
    +

    a seed to be set before model fitting, marginal likelihood +computation, and posterior mixing for reproducibility of results. Defaults +to NULL - no seed is set.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    BiBMA returns an object of class 'BiBMA'.

    +
    +
    +

    Details

    +

    See BiBMA() for more details.

    +
    +
    +

    See also

    +

    BiBMA(), summary.RoBMA(), prior(), check_setup()

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/update.RoBMA.html b/docs/reference/update.RoBMA.html new file mode 100644 index 00000000..aa7b5569 --- /dev/null +++ b/docs/reference/update.RoBMA.html @@ -0,0 +1,327 @@ + +Updates a fitted RoBMA object — update.RoBMA • RoBMA + Skip to contents + + +
    +
    +
    + +

    Updates a fitted RoBMA object

    + Source: R/RoBMA.R +
    update.RoBMA.Rd
    +
    + +
    +

    update.RoBMA can be used to

    1. add an additional model to an existing "RoBMA" object by +specifying either a null or alternative prior for each parameter +and the prior odds of the model (prior_weights), see the +vignette("CustomEnsembles") vignette,

      2. +

    2. change the prior odds of fitted models by specifying a vector +prior_weights of the same length as the fitted models,

      3. +

    3. refitting models that failed to converge with updated settings +of control parameters,

      4. +

    4. or changing the convergence criteria and recalculating the ensemble +results by specifying new control argument and setting +refit_failed == FALSE.

      5. +
    + +
    +

    Usage

    + 
    # S3 method for class 'RoBMA'
+update(
+  object,
+  refit_failed = TRUE,
+  extend_all = FALSE,
+  prior_effect = NULL,
+  prior_heterogeneity = NULL,
+  prior_bias = NULL,
+  prior_hierarchical = NULL,
+  prior_weights = NULL,
+  prior_effect_null = NULL,
+  prior_heterogeneity_null = NULL,
+  prior_bias_null = NULL,
+  prior_hierarchical_null = NULL,
+  study_names = NULL,
+  chains = NULL,
+  adapt = NULL,
+  burnin = NULL,
+  sample = NULL,
+  thin = NULL,
+  autofit = NULL,
+  parallel = NULL,
+  autofit_control = NULL,
+  convergence_checks = NULL,
+  save = "all",
+  seed = NULL,
+  silent = TRUE,
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    a fitted RoBMA object

    + + +
    refit_failed
    +

    whether failed models should be refitted. Relevant only +if new priors or prior_weights are not supplied. Defaults to TRUE.

    + + +
    extend_all
    +

    extend sampling in all fitted models based on "sample_extend" +argument in set_autofit_control() function. Defaults to FALSE.

    + + +
    prior_effect
    +

    prior distribution for the effect size (mu) +parameter that will be treated as belonging to the alternative hypothesis. +Defaults to NULL.

    + + +
    prior_heterogeneity
    +

    prior distribution for the heterogeneity tau +parameter that will be treated as belonging to the alternative hypothesis. +Defaults to NULL.

    + + +
    prior_bias
    +

    prior distribution for the publication bias adjustment +component that will be treated as belonging to the alternative hypothesis. +Defaults to NULL.

    + + +
    prior_hierarchical
    +

    prior distribution for the correlation of random effects +(rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows +users to fit a hierarchical (three-level) meta-analysis when study_ids are supplied. +Note that this is an experimental feature and see News for more details. Defaults to a beta distribution +prior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).

    + + +
    prior_weights
    +

    either a single value specifying prior model weight +of a newly specified model using priors argument, or a vector of the +same length as already fitted models to update their prior weights.

    + + +
    prior_effect_null
    +

    prior distribution for the effect size (mu) +parameter that will be treated as belonging to the null hypothesis. +Defaults to NULL.

    + + +
    prior_heterogeneity_null
    +

    prior distribution for the heterogeneity tau +parameter that will be treated as belonging to the null hypothesis. +Defaults to NULL.

    + + +
    prior_bias_null
    +

    prior distribution for the publication bias adjustment +component that will be treated as belonging to the null hypothesis. +Defaults to NULL.

    + + +
    prior_hierarchical_null
    +

    prior distribution for the correlation of random effects +(rho) parameter that will be treated as belonging to the null hypothesis. Defaults to NULL.

    + + +
    study_names
    +

    an optional argument with the names of the studies

    + + +
    chains
    +

    a number of chains of the MCMC algorithm.

    + + +
    adapt
    +

    a number of adaptation iterations of the MCMC algorithm. +Defaults to 500.

    + + +
    burnin
    +

    a number of burnin iterations of the MCMC algorithm. +Defaults to 2000.

    + + +
    sample
    +

    a number of sampling iterations of the MCMC algorithm. +Defaults to 5000.

    + + +
    thin
    +

    a thinning of the chains of the MCMC algorithm. Defaults to +1.

    + + +
    autofit
    +

    whether the model should be fitted until the convergence +criteria (specified in autofit_control) are satisfied. Defaults to +TRUE.

    + + +
    parallel
    +

    whether the individual models should be fitted in parallel. +Defaults to FALSE. The implementation is not completely stable +and might cause a connection error.

    + + +
    autofit_control
    +

    allows to pass autofit control settings with the +set_autofit_control() function. See ?set_autofit_control for +options and default settings.

    + + +
    convergence_checks
    +

    automatic convergence checks to assess the fitted +models, passed with set_convergence_checks() function. See +?set_convergence_checks for options and default settings.

    + + +
    save
    +

    whether all models posterior distributions should be kept +after obtaining a model-averaged result. Defaults to "all" which +does not remove anything. Set to "min" to significantly reduce +the size of final object, however, some model diagnostics and further +manipulation with the object will not be possible.

    + + +
    seed
    +

    a seed to be set before model fitting, marginal likelihood +computation, and posterior mixing for reproducibility of results. Defaults +to NULL - no seed is set.

    + + +
    silent
    +

    whether all print messages regarding the fitting process +should be suppressed. Defaults to TRUE. Note that parallel = TRUE +also suppresses all messages.

    + + +
    ...
    +

    additional arguments.

    + +
    +
    +

    Value

    +

    RoBMA returns an object of class 'RoBMA'.

    +
    +
    +

    Details

    +

    See RoBMA() for more details.

    +
    +
    +

    See also

    +

    RoBMA(), summary.RoBMA(), prior(), check_setup()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+# using the example data from Bem 2011 and fitting the default (RoBMA-PSMA) model
+fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study)
+
+# the update function allows us to change the prior model weights of each model
+fit1 <- update(fit, prior_weights = c(0, rep(1, 35)))
+
+# add an additional model with different priors specification
+# (see '?prior' for more information)
+fit2 <- update(fit,
+               priors_effect_null = prior("point", parameters = list(location = 0)),
+               priors_heterogeneity = prior("normal",
+                                  parameters = list(mean = 0, sd = 1),
+                                  truncation = list(lower = 0, upper = Inf)),
+               priors_bias = prior_weightfunction("one-sided",
+                                    parameters = list(cuts = c(.05, .10, .20),
+                                                      alpha = c(1, 1, 1, 1))))
+
+# update the models with an increased number of sample iterations
+fit3 <- update(fit, autofit_control = set_autofit_control(sample_extend = 1000), extend_all = TRUE)
+} # }
+
+
+
    +
    +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/weighted_multivariate_normal.html b/docs/reference/weighted_multivariate_normal.html new file mode 100644 index 00000000..c5991db9 --- /dev/null +++ b/docs/reference/weighted_multivariate_normal.html @@ -0,0 +1,124 @@ + +Weighted multivariate normal distribution — weighted_multivariate_normal • RoBMA + Skip to contents + + +
    +
    +
    + +

    Weighted multivariate normal distribution

    + Source: R/distributions.R +
    weighted_multivariate_normal.Rd
    +
    + +
    +

    Density function for the weighted multivariate normal +distribution with mean, covariance matrix sigma, +critical values crit_x, and weights omega.

    +
    + + +
    +

    Arguments

    + + +
    x
    +

    quantiles.

    + + +
    p
    +

    vector of probabilities.

    + + +
    mean
    +

    mean

    + + +
    sigma
    +

    covariance matrix.

    + + +
    crit_x
    +

    vector of critical values defining steps.

    + + +
    omega
    +

    vector of weights defining the probability +of observing a t-statistics between each of the two steps.

    + + +
    type
    +

    type of weight function (defaults to "two.sided").

    + + +
    log, log.p
    +

    logical; if TRUE, probabilities +p are given as log(p).

    + +
    +
    +

    Value

    +

    .dwmnorm_fast returns a density of the multivariate +weighted normal distribution.

    +
    +
    +

    See also

    +

    Normal, weighted_normal

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/weighted_normal.html b/docs/reference/weighted_normal.html new file mode 100644 index 00000000..b7ee95aa --- /dev/null +++ b/docs/reference/weighted_normal.html @@ -0,0 +1,197 @@ + +Weighted normal distribution — weighted_normal • RoBMA + Skip to contents + + +
    +
    +
    + +

    Weighted normal distribution

    + Source: R/distributions.R +
    weighted_normal.Rd
    +
    + +
    +

    Density, distribution function, quantile function +and random generation for the weighted normal distribution with +mean, standard deviation sd, steps steps +(or critical values) crit_x), and weights omega.

    +
    + +
    +

    Usage

    + 
    dwnorm(
+  x,
+  mean,
+  sd,
+  steps = if (!is.null(crit_x)) NULL,
+  omega,
+  crit_x = if (!is.null(steps)) NULL,
+  type = "two.sided",
+  log = FALSE
+)
+
+pwnorm(
+  q,
+  mean,
+  sd,
+  steps = if (!is.null(crit_x)) NULL,
+  omega,
+  crit_x = if (!is.null(steps)) NULL,
+  type = "two.sided",
+  lower.tail = TRUE,
+  log.p = FALSE
+)
+
+qwnorm(
+  p,
+  mean,
+  sd,
+  steps = if (!is.null(crit_x)) NULL,
+  omega,
+  crit_x = if (!is.null(steps)) NULL,
+  type = "two.sided",
+  lower.tail = TRUE,
+  log.p = FALSE
+)
+
+rwnorm(
+  n,
+  mean,
+  sd,
+  steps = if (!is.null(crit_x)) NULL,
+  omega,
+  crit_x = if (!is.null(steps)) NULL,
+  type = "two.sided"
+)
    +
    + +
    +

    Arguments

    + + +
    x, q
    +

    vector of quantiles.

    + + +
    mean
    +

    mean

    + + +
    sd
    +

    standard deviation.

    + + +
    steps
    +

    vector of steps for the weight function.

    + + +
    omega
    +

    vector of weights defining the probability +of observing a t-statistics between each of the two steps.

    + + +
    crit_x
    +

    vector of critical values defining steps +(if steps are not supplied).

    + + +
    type
    +

    type of weight function (defaults to "two.sided").

    + + +
    log, log.p
    +

    logical; if TRUE, probabilities +p are given as log(p).

    + + +
    lower.tail
    +

    logical; if TRUE (default), probabilities +are \(P[X \le x]\), otherwise, \(P[X \ge x]\).

    + + +
    p
    +

    vector of probabilities.

    + + +
    n
    +

    number of observations. If length(n) > 1, the length +is taken to be the number required.

    + +
    +
    +

    Value

    +

    dwnorm gives the density, dwnorm gives the +distribution function, qwnorm gives the quantile function, +and rwnorm generates random deviates.

    +
    +
    +

    Details

    +

    The mean, sd, steps, omega can be +supplied as a vectors (mean, sd) or matrices (steps, +omega) with length / number of rows equal to x/q/ +p. Otherwise, they are recycled to the length of the result.

    +
    +
    +

    See also

    +

    Normal

    +
    + +
    + + +
    +

    Developed by František Bartoš, Maximilian Maier.

    +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + diff --git a/docs/reference/z2OR.html b/docs/reference/z2OR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/z2OR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/z2d.html b/docs/reference/z2d.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/z2d.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/z2logOR.html b/docs/reference/z2logOR.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/z2logOR.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/reference/z2r.html b/docs/reference/z2r.html new file mode 100644 index 00000000..a807e9b6 --- /dev/null +++ b/docs/reference/z2r.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/search.json b/docs/search.json new file mode 100644 index 00000000..3a30c84c --- /dev/null +++ b/docs/search.json @@ -0,0 +1 @@ +[{"path":"https://https://fbartos.github.io/RoBMA/articles/CustomEnsembles.html","id":"the-dataset","dir":"Articles","previous_headings":"","what":"The Dataset","title":"Fitting Custom Meta-Analytic Ensembles","text":"illustrate custom model building procedure, use data infamous Bem (2011) “Feeling future” precognition study. use coding results summarized Bem one later replies (Bem et al., 2011).","code":"library(RoBMA) #> Loading required namespace: runjags #> Loading required namespace: mvtnorm data(\"Bem2011\", package = \"RoBMA\") Bem2011 #> d se study #> 1 0.25 0.10155048 Detection of Erotic Stimuli #> 2 0.20 0.08246211 Avoidance of Negative Stimuli #> 3 0.26 0.10323629 Retroactive Priming I #> 4 0.23 0.10182427 Retroactive Priming II #> 5 0.22 0.10120277 Retroactive Habituation I - Negative trials #> 6 0.15 0.08210765 Retroactive Habituation II - Negative trials #> 7 0.09 0.07085372 Retroactive Induction of Boredom #> 8 0.19 0.10089846 Facilitation of Recall I #> 9 0.42 0.14752627 Facilitation of Recall II"},{"path":"https://https://fbartos.github.io/RoBMA/articles/CustomEnsembles.html","id":"the-custom-ensemble","dir":"Articles","previous_headings":"","what":"The Custom Ensemble","title":"Fitting Custom Meta-Analytic Ensembles","text":"consider following scenarios plausible explanations data, decide include models meta-analytic ensemble: absolutely precognition effect - fixed effects model assuming effect size zero (H0fH_{0}^f), experiments measured underlying precognition effect - fixed effects model (H1fH_{1}^f), experiments measured slightly different precognition effect - random effects model (H1rH_{1}^r), absolutely precognition effect results can explained publication bias, modeled one following publication bias adjustments: - 4.1) one-sided selection operating significant p-values (H1,pb1fH_{1,\\text{pb1}}^f), - 4.2) one-sided selection operating significant marginally significant p-values (H1,pb2fH_{1,\\text{pb2}}^f), - 4.3) PET correction publication bias adjusts relationship effect sizes standard errors (H1,pb3fH_{1,\\text{pb3}}^f), - 4.4) PEESE correction publication bias adjusts relationship effect sizes standard errors squared (H1,pb4fH_{1,\\text{pb4}}^f). fit ensemble using RoBMA() function specifying priors, ended 2 (effect effect) * 2 (heterogeneity heterogeneity) * 5 (publication bias 4 ways adjusting publication bias) = 20 models. 13 models requested. Furthermore, specify different parameters prior distributions model. following process allows , though utilize . start fitting first model using RoBMA() function continuously update fitted object include models.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/CustomEnsembles.html","id":"model-1","dir":"Articles","previous_headings":"The Custom Ensemble","what":"Model 1","title":"Fitting Custom Meta-Analytic Ensembles","text":"initiate model ensemble specifying first model RoBMA() function. explicitly specify prior distributions components set prior distributions correspond null hypotheses set seed ensure reproducibility results. verify ensemble contains single specified model summary() function setting type = \"models\".","code":"fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, priors_effect_null = prior(\"spike\", parameters = list(location = 0)), priors_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), priors_bias_null = prior_none(), seed = 1) summary(fit, type = \"models\") #> Call: #> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, #> priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, #> priors_effect_null = prior(\"spike\", parameters = list(location = 0)), #> priors_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), #> priors_bias_null = prior_none(), seed = 1) #> #> Robust Bayesian meta-analysis #> Models overview: #> Model Prior Effect Prior Heterogeneity Prior prob. log(marglik) Post. prob. #> 1 Spike(0) Spike(0) 1.000 -3.28 1.000 #> Inclusion BF #> Inf"},{"path":"https://https://fbartos.github.io/RoBMA/articles/CustomEnsembles.html","id":"model-2","dir":"Articles","previous_headings":"The Custom Ensemble","what":"Model 2","title":"Fitting Custom Meta-Analytic Ensembles","text":"add second model ensemble, need decide prior distribution mean parameter. precognition exist, effect small since casinos bankrupted otherwise. effect also positive, since deviation randomness characterized effect. Therefore, decide use normal distribution mean = 0.15, standard deviation 0.10, truncated positive range. sets prior density around small effect sizes. get better grasp prior distribution, visualize using plot()) function (figure can also created using ggplot2 package adding plot_type = \"ggplot\" argument). add second model ensemble using update.RoBMA() function. function can also used many purposes - updating settings, prior model weights, refitting failed models. , supply fitted ensemble object add argument specifying prior distributions component additional model. Since want add Model 2 - set prior μ\\mu parameter treated prior belonging alternative hypothesis effect size component remaining priors treated belonging null hypotheses. wanted, also specify prior_weights argument, change prior probability fitted model utilize option keep default value, sets prior weights new model 1. (Note arguments specifying prior distributions update.RoBMA() function prior_X - singular, comparison RoBMA() function uses priors_X plural.) can inspect updated ensemble verify contains models. see Model 2 notably outperformed first model attained posterior model probability.","code":"plot(prior(\"normal\", parameters = list(mean = .15, sd = .10), truncation = list(lower = 0))) fit <- update(fit, prior_effect = prior(\"normal\", parameters = list(mean = .15, sd = .10), truncation = list(lower = 0)), prior_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), prior_bias_null = prior_none()) summary(fit, type = \"models\") #> Call: #> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, #> priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, #> priors_effect_null = prior(\"spike\", parameters = list(location = 0)), #> priors_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), #> priors_bias_null = prior_none(), seed = 1) #> #> Robust Bayesian meta-analysis #> Models overview: #> Model Prior Effect Prior Heterogeneity Prior prob. log(marglik) #> 1 Spike(0) Spike(0) 0.500 -3.28 #> 2 Normal(0.15, 0.1)[0, Inf] Spike(0) 0.500 14.91 #> Post. prob. Inclusion BF #> 0.000 0.000 #> 1.000 79422247.251"},{"path":"https://https://fbartos.github.io/RoBMA/articles/CustomEnsembles.html","id":"models-3-4-4","dir":"Articles","previous_headings":"The Custom Ensemble","what":"Models 3-4.4","title":"Fitting Custom Meta-Analytic Ensembles","text":"add remaining models ensemble using update() function, need decide remaining prior distributions. Specifically, prior distribution heterogeneity parameter τ\\tau, publication bias adjustment parameters ω\\omega (selection models’ weightfunctions) PET PEESE PET PEESE adjustment. Model 3, use usual inverse-gamma(1, .15) prior distribution based empirical heterogeneity estimates (Erp et al., 2017) heterogeneity parameter τ\\tau. Models 4.1-4.4 use default settings publication bias adjustments outlined Appendix B (Bartoš et al., 2023). Now, just need add remaining models ensemble using update() function already illustrated. verify requested models included ensemble using summary()) function type = \"models\" argument.","code":"### adding Model 3 fit <- update(fit, prior_effect = prior(\"normal\", parameters = list(mean = .15, sd = .10), truncation = list(lower = 0)), prior_heterogeneity = prior(\"invgamma\", parameters = list(shape = 1, scale = .15)), prior_bias_null = prior_none()) ### adding Model 4.1 fit <- update(fit, prior_effect_null = prior(\"spike\", parameters = list(location = 0)), prior_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), prior_bias = prior_weightfunction(\"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)))) ### adding Model 4.2 fit <- update(fit, prior_effect_null = prior(\"spike\", parameters = list(location = 0)), prior_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), prior_bias = prior_weightfunction(\"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)))) ### adding Model 4.3 fit <- update(fit, prior_effect_null = prior(\"spike\", parameters = list(location = 0)), prior_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), prior_bias = prior_PET(\"Cauchy\", parameters = list(0, 1), truncation = list(lower = 0))) ### adding Model 4.4 fit <- update(fit, prior_effect_null = prior(\"spike\", parameters = list(location = 0)), prior_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), prior_bias = prior_PEESE(\"Cauchy\", parameters = list(0, 5), truncation = list(lower = 0))) summary(fit, type = \"models\") #> Call: #> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, #> priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, #> priors_effect_null = prior(\"spike\", parameters = list(location = 0)), #> priors_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), #> priors_bias_null = prior_none(), seed = 1) #> #> Robust Bayesian meta-analysis #> Models overview: #> Model Prior Effect Prior Heterogeneity #> 1 Spike(0) Spike(0) #> 2 Normal(0.15, 0.1)[0, Inf] Spike(0) #> 3 Normal(0.15, 0.1)[0, Inf] InvGamma(1, 0.15) #> 4 Spike(0) Spike(0) #> 5 Spike(0) Spike(0) #> 6 Spike(0) Spike(0) #> 7 Spike(0) Spike(0) #> Prior Bias Prior prob. log(marglik) #> 0.143 -3.28 #> 0.143 14.91 #> 0.143 12.85 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.143 13.70 #> omega[one-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.143 12.58 #> PET ~ Cauchy(0, 1)[0, Inf] 0.143 15.75 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.143 15.65 #> Post. prob. Inclusion BF #> 0.000 0.000 #> 0.168 1.210 #> 0.021 0.132 #> 0.050 0.318 #> 0.016 0.100 #> 0.391 3.845 #> 0.353 3.278"},{"path":"https://https://fbartos.github.io/RoBMA/articles/CustomEnsembles.html","id":"using-the-fitted-ensemble","dir":"Articles","previous_headings":"","what":"Using the Fitted Ensemble","title":"Fitting Custom Meta-Analytic Ensembles","text":"Finally, use summary() function inspect model results. results custom ensemble indicate weak evidence absence precognition effect, BF10=0.584\\text{BF}_{10} = 0.584 -> BF01=1.71\\text{BF}_{01} = 1.71, moderate evidence absence heterogeneity, BFrf=0.132\\text{BF}_{\\text{rf}} = 0.132 -> BFfr=7.58\\text{BF}_{\\text{fr}} = 7.58, moderate evidence presence publication bias, BFpb=3.21\\text{BF}_{\\text{pb}} = 3.21. finalized ensemble can treated RoBMA ensemble using summary(), plot(), plot_models(), forest(), diagnostics() functions. example, can use plot.RoBMA() parameter = \"mu\", prior = TRUE arguments plot prior (grey) posterior distribution (black) effect size. function visualizes model-averaged estimates across models default. arrows represent probability mass value 0 (spike 0). secondary y-axis (right) shows probability mass zero effect size, increased prior probability 0.71 posterior posterior probability 0.81. can also inspect posterior distributions publication bias adjustments. visualize model-averaged weightfunction, set parameter = weightfunction argument. resulting figure shows light gray prior distribution dark gray posterior distribution. can also inspect posterior estimate regression relationship standard errors effect sizes setting parameter = \"PET-PEESE\".","code":"summary(fit) #> Call: #> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, #> priors_effect = NULL, priors_heterogeneity = NULL, priors_bias = NULL, #> priors_effect_null = prior(\"spike\", parameters = list(location = 0)), #> priors_heterogeneity_null = prior(\"spike\", parameters = list(location = 0)), #> priors_bias_null = prior_none(), seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/7 0.286 0.189 0.584 #> Heterogeneity 1/7 0.143 0.021 0.132 #> Bias 4/7 0.571 0.811 3.212 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.036 0.000 0.000 0.226 #> tau 0.002 0.000 0.000 0.000 #> omega[0,0.05] 1.000 1.000 1.000 1.000 #> omega[0.05,0.1] 0.938 1.000 0.014 1.000 #> omega[0.1,1] 0.935 1.000 0.012 1.000 #> PET 0.820 0.000 0.000 2.601 #> PEESE 7.284 0.000 0.000 25.508 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). plot(fit, parameter = \"mu\", prior = TRUE) plot(fit, parameter = \"weightfunction\", prior = TRUE) plot(fit, parameter = \"PET-PEESE\", prior = TRUE)"},{"path":"https://https://fbartos.github.io/RoBMA/articles/CustomEnsembles.html","id":"footnotes","dir":"Articles","previous_headings":"","what":"Footnotes","title":"Fitting Custom Meta-Analytic Ensembles","text":"1^1 - default setting used produce 12 models RoBMA versions < 2, corresponded earlier article Maier et al. (2023) applied Bayesian model-averaging across selection models.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"example-data-set","dir":"Articles","previous_headings":"","what":"Example Data Set","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"use dat.konstantopoulos2011 data set metadat R package (Thomas et al., 2019) used functionality metafor (Wolfgang, 2010) R package. roughly follow example data set’s help file, ?dat.konstantopoulos2011. data set consists 56 studies estimating effects modified school calendars students’ achievement. 56 studies run individual schools, can grouped 11 districts. might expect similar effect size estimates schools district – words, effect size estimates district might completely independent. Consequently, might want adjust dependency (clustering) effect size estimates draw appropriate inference. First, load data set, assign dat object, inspect first rows. following analyses, use following variables: yi, standardized mean differences, vi, sampling variances standardized mean differences, district, district id distinguishes among districts, school, distinguishes among different schools within district.","code":"data(\"dat.konstantopoulos2011\", package = \"metadat\") dat <- dat.konstantopoulos2011 head(dat) #> district school study year yi vi #> 1 11 1 1 1976 -0.18 0.118 #> 2 11 2 2 1976 -0.22 0.118 #> 3 11 3 3 1976 0.23 0.144 #> 4 11 4 4 1976 -0.30 0.144 #> 5 12 1 5 1989 0.13 0.014 #> 6 12 2 6 1989 -0.26 0.014"},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"frequentist-hierarchical-meta-analysis-with-metafor","dir":"Articles","previous_headings":"","what":"Frequentist Hierarchical Meta-Analysis with metafor","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"follow data set’s help file fit simple random effects meta-analysis using rma() function metafor package. model ignores dependency effect size estimates. use simple model starting point comparison later models. model summary returns small statistically significant effect size estimate μ=0.128\\mu = 0.128 (se=0.044\\text{se} = 0.044) considerable heterogeneity estimate τ=0.297\\tau = 0.297. extend model account hierarchical structure data, .e., schools within districts, using rma.mv() function metafor package extending random = ~ school | district argument. find accounting hierarchical structure data results (1) slightly larger effect size estimate (μ=0.187\\mu = 0.187) (2) larger standard error effect size estimate (se=0.085\\text{se} = 0.085). larger standard error natural consequence accounting dependency effect sizes. effect sizes dependent, contribute less additional information independent effect sizes . Specifying hierarchical model accounts dependency estimating similarity estimates cluster (school) discounting information borrowed estimate. estimate similarity among estimates cluster summarized \\rho = 0.666 estimate.","code":"fit_metafor.0 <- metafor::rma(yi = yi, vi = vi, data = dat) fit_metafor.0 #> #> Random-Effects Model (k = 56; tau^2 estimator: REML) #> #> tau^2 (estimated amount of total heterogeneity): 0.0884 (SE = 0.0202) #> tau (square root of estimated tau^2 value): 0.2974 #> I^2 (total heterogeneity / total variability): 94.70% #> H^2 (total variability / sampling variability): 18.89 #> #> Test for Heterogeneity: #> Q(df = 55) = 578.8640, p-val < .0001 #> #> Model Results: #> #> estimate se zval pval ci.lb ci.ub #> 0.1279 0.0439 2.9161 0.0035 0.0419 0.2139 ** #> #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 fit_metafor <- metafor::rma.mv(yi, vi, random = ~ school | district, data = dat) fit_metafor #> #> Multivariate Meta-Analysis Model (k = 56; method: REML) #> #> Variance Components: #> #> outer factor: district (nlvls = 11) #> inner factor: school (nlvls = 11) #> #> estim sqrt fixed #> tau^2 0.0978 0.3127 no #> rho 0.6653 no #> #> Test for Heterogeneity: #> Q(df = 55) = 578.8640, p-val < .0001 #> #> Model Results: #> #> estimate se zval pval ci.lb ci.ub #> 0.1847 0.0846 2.1845 0.0289 0.0190 0.3504 * #> #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"specifications-of-hierarchical-meta-analysis","dir":"Articles","previous_headings":"","what":"Specifications of Hierarchical Meta-Analysis","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"specify simple hierarchical meta-analytic model (see Konstantopoulos (2011) example). Using distributional notation, can describe data generating process multi-stage sampling procedure. nutshell, assume existence overall mean effect μ\\mu. Next, assume effect sizes district k=1,…,Kk = 1, \\dots, K, γk\\gamma_k, systematically differ mean effect, variance district-level effects summarized heterogeneity τb\\tau_{b} (). Furthermore, assume true effects θk,j\\theta_{k,j} study j=1,…Jkj = 1, \\dots J_k systematically differ district-level effect, variance study effects district-level effect summarized heterogeneity τw\\tau_{w} (within). Finally, observed effect sizes yk,jy_{k,j} differ true effects yk,jy_{k,j} due random errors sek,j\\text{se}_{k,j}. Mathematically, can describe model : γk∼N(μ,τb2),θk,j∼N(γk,τw2),yk,j∼N(θk,j,sek,j). \\begin{aligned} \\gamma_k &\\sim \\text{N}(\\mu, \\tau_b^2),\\\\ \\theta_{k,j} &\\sim \\text{N}(\\gamma_k, \\tau_w^2),\\\\ y_{k,j} &\\sim \\text{N}(\\theta_{k,j}, \\text{se}_{k,j}).\\\\ \\end{aligned} N() denotes normal distribution mean variance. Conveniently, bit algebra, need estimate district-level true study effects. Instead, marginalize , sample observed effect sizes district yk,.y_{k,.} directly multivariate normal distributions, MN(), common mean μ\\mu covariance matrix S: yk,.∼MN(μ,S),S=[τb2+τw2+se12τw2…τw2τw2τb2+τw2+se22…τw2…………τw2τw2…τb2+τw2+seJk2]. \\begin{aligned} y_{k,.} &\\sim \\text{MN}(\\mu, \\text{S}),\\\\ \\text{S} &= \\begin{bmatrix} \\tau_b^2 + \\tau_w^2 + \\text{se}_1^2 & \\tau_w^2 & \\dots & \\tau_w^2 \\\\ \\tau_w^2 & \\tau_b^2 + \\tau_w^2 + \\text{se}_2^2 & \\dots & \\tau_w^2 \\\\ \\dots & \\dots & \\dots & \\dots \\\\ \\tau_w^2 & \\tau_w^2 & \\dots & \\tau_b^2 + \\tau_w^2 + \\text{se}_{J_k}^2 & \\\\ \\end{bmatrix}. \\end{aligned} random effects marginalization helpful allows us sample fewer parameters posterior distribution (significantly simplifies marginal likelihood estimation via bridge sampling). Furthermore, marginalization allows us properly specify selection model publication bias adjustment models – marginalization propagates selection process sampling steps (proceed sequential sampling selection procedure observed effect sizes modifies sampling distributions preceding levels). can re-parameterize model performing following substitution, τ2=τb2+τw2,ρ=τw2τb2+τw2, \\begin{aligned} \\tau^2 &= \\tau_b^2 + \\tau_w^2,\\\\ \\rho &= \\frac{\\tau_w^2}{\\tau_b^2 + \\tau_w^2}, \\end{aligned} specifying covariance matrix using inter-study correlation ρ\\rho, total heterogeneity τ\\tau, standard errors se.\\text{se}_{.}: S=[τ2+se12ρτ2…ρτ2ρτ2τ2+se22…ρτ2…………ρτ2ρτ2…τ2+seJk2]. \\begin{aligned} \\text{S} &= \\begin{bmatrix} \\tau^2 + \\text{se}_1^2 & \\rho\\tau^2 & \\dots & \\rho\\tau^2 \\\\ \\rho\\tau^2 & \\tau^2 + \\text{se}_2^2 & \\dots & \\rho\\tau^2 \\\\ \\dots & \\dots & \\dots & \\dots \\\\ \\rho\\tau^2 & \\rho\\tau^2 & \\dots & \\tau^2 + \\text{se}_{J_k}^2 & \\\\ \\end{bmatrix}. \\end{aligned} specification corresponds compound symmetry covariance matrix random effects, default settings metafor::rma.mv() function. importantly, allows us easily specify prior distributions correlation coefficient ρ\\rho total heterogeneity τ\\tau.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"hierarchical-bayesian-model-averaged-meta-analysis-with-robma","dir":"Articles","previous_headings":"","what":"Hierarchical Bayesian Model-Averaged Meta-Analysis with RoBMA","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"estimate complete Hierarchical Bayesian Model-Averaged Meta-Analysis (hBMA) RoBMA package, briefly reproduce simpler models estimated metafor package previous section.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"bayesian-random-effects-meta-analysis","dir":"Articles","previous_headings":"Hierarchical Bayesian Model-Averaged Meta-Analysis with RoBMA","what":"Bayesian Random Effects Meta-Analysis","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"First, estimate simple Bayesian random effects meta-analysis (corresponding fit_metafor.0). use RoBMA() function specify effect sizes sampling variances via d = dat$yi v = dat$vi arguments. set priors_effect_null, priors_heterogeneity_null, priors_bias arguments null omit models assuming absence effect, heterogeneity, publication bias adjustment components. generate complete summary estimated model adding type = \"individual\" argument summary() function. verify effect size, μ=0.126\\mu = 0.126 (95% CI [0.041,0.211]\\text{95% CI } [0.041, 0.211]), heterogeneity, τ=0.292\\tau = 0.292 (95% CI [0.233,0.364]\\text{95% CI } [0.233, 0.364]), estimates closely correspond frequentist results (expect parameter estimates weakly informative priors).","code":"fit.0 <- RoBMA(d = dat$yi, v = dat$vi, priors_effect_null = NULL, priors_heterogeneity_null = NULL, priors_bias = NULL, parallel = TRUE, seed = 1) summary(fit.0, type = \"individual\") #> Call: #> RoBMA(d = dat$yi, v = dat$vi, priors_bias = NULL, priors_effect_null = NULL, #> priors_heterogeneity_null = NULL, parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Model 1 Parameter prior distributions #> Prior prob. 1.000 mu ~ Normal(0, 1) #> log(marglik) 17.67 tau ~ InvGamma(1, 0.15) #> Post. prob. 1.000 #> Inclusion BF Inf #> #> Parameter estimates: #> Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat #> mu 0.126 0.043 0.041 0.127 0.211 0.00044 0.010 9757 1.000 #> tau 0.292 0.033 0.233 0.290 0.364 0.00034 0.010 9678 1.000 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"hierarchical-bayesian-random-effects-meta-analysis","dir":"Articles","previous_headings":"Hierarchical Bayesian Model-Averaged Meta-Analysis with RoBMA","what":"Hierarchical Bayesian Random Effects Meta-Analysis","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"Second, account clustered effect size estimates within districts extending previous function call study_ids = dat$district argument. allows us estimate hierarchical Bayesian random effects meta-analysis (corresponding fit_metafor). use default prior distribution correlation parameter \\rho \\sim \\text{Beta}(1, 1), set via priors_hierarchical argument, restricts correlation positive uniformly distributed interval (0,1)(0, 1). , generate complete summary estimated model, verify estimates, , correspond frequentist counterparts, estimated effect size, μ=0.181\\mu = 0.181 (95% CI [0.017,0.346]\\text{95% CI } [0.017, 0.346]), heterogeneity, τ=0.308\\tau = 0.308 (95% CI [0.223,0.442]\\text{95% CI } [0.223, 0.442]), correlation, ρ=0.627\\rho = 0.627 (95% CI [0.320,0.864]\\text{95% CI } [0.320, 0.864]). can visualize prior posterior distribution ρ\\rho parameter using plot() function.","code":"fit <- RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_effect_null = NULL, priors_heterogeneity_null = NULL, priors_bias = NULL, parallel = TRUE, seed = 1) summary(fit, type = \"individual\") #> Call: #> RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_bias = NULL, #> priors_effect_null = NULL, priors_heterogeneity_null = NULL, #> parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Model 1 Parameter prior distributions #> Prior prob. 1.000 mu ~ Normal(0, 1) #> log(marglik) 25.70 tau ~ InvGamma(1, 0.15) #> Post. prob. 1.000 rho ~ Beta(1, 1) #> Inclusion BF Inf #> #> Parameter estimates: #> Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat #> mu 0.181 0.083 0.017 0.180 0.346 0.00088 0.011 9041 1.000 #> tau 0.308 0.056 0.223 0.299 0.442 0.00090 0.016 3859 1.000 #> rho 0.627 0.142 0.320 0.641 0.864 0.00219 0.015 4202 1.000 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). par(mar = c(2, 4, 0, 0)) plot(fit, parameter = \"rho\", prior = TRUE)"},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"hierarchical-bayesian-model-averaged-meta-analysis","dir":"Articles","previous_headings":"Hierarchical Bayesian Model-Averaged Meta-Analysis with RoBMA","what":"Hierarchical Bayesian Model-Averaged Meta-Analysis","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"Third, extend previous model model ensemble also includes models assuming absence effect /heterogeneity (incorporate models assuming presence publication bias due computational complexity explained summary). Including additional models allows us evaluate evidence favor effect heterogeneity. Furthermore, specifying additional models allows us incorporate uncertainty specified models weight posterior distribution according well models predicted data. estimate remaining models removing priors_effect_null priors_heterogeneity_null arguments previous function calls, include previously omitted models effect /heterogeneity. Now generate summary complete model-averaged ensemble specifying additional arguments summary() function. find ensemble contains four models, combination models assuming presence/absence effect/heterogeneity, equal prior model probabilities. Importantly, models assuming heterogeneity also specified hierarchical structure account clustering. comparison specified models reveals weak evidence effect, BF10=0.917\\text{BF}_{10} = 0.917, extreme evidence presence heterogeneity, BFrf=9.3×1092\\text{BF}_{\\text{rf}} = 9.3\\times10^{92}. Moreover, find Hierarchical component summary values Heterogeneity component summary default settings specify models assuming presence heterogeneity also include hierarchical structure. also obtain model-averaged posterior estimates combine posterior estimates models according posterior model probabilities, effect size, μ=0.087\\mu = 0.087 (95% CI [0.000,0.314]\\text{95% CI } [0.000, 0.314]), heterogeneity, τ=0.326\\tau = 0.326 (95% CI [0.231,0.472]\\text{95% CI } [0.231, 0.472]), correlation, ρ=0.659\\rho = 0.659 (95% CI [0.354,0.879]\\text{95% CI } [0.354, 0.879]).","code":"fit_BMA <- RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_bias = NULL, parallel = TRUE, seed = 1) summary(fit_BMA) #> Call: #> RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_bias = NULL, #> parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/4 0.500 0.478 9.170000e-01 #> Heterogeneity 2/4 0.500 1.000 9.326943e+92 #> Hierarchical 2/4 0.500 1.000 9.326943e+92 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.087 0.000 0.000 0.314 #> tau 0.326 0.317 0.231 0.472 #> rho 0.659 0.675 0.354 0.879 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"testing-the-presence-of-clustering","dir":"Articles","previous_headings":"Hierarchical Bayesian Model-Averaged Meta-Analysis with RoBMA","what":"Testing the Presence of Clustering","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"previous analyses, assumed effect sizes indeed clustered within districts, adjusted clustering. However, effect sizes within cluster may similar effect sizes different clusters. Now, specify model ensemble allows us test assumption specifying two sets random effect meta-analytic models. first set models assumes indeed clustering correlation random effects uniformly distributed (0,1)(0, 1) interval (previous analyses). second set models assumes clustering, .e., correlation random effects ρ=0\\rho = 0, simplifies structured covariance matrix diagonal matrix. , model average across models assuming presence absence effect account model uncertainty. specify ‘special’ model ensemble RoBMA() function, need modify previous model call following ways. removed fixed effect models specifying priors_heterogeneity_null = NULL argument.1^1 Furthermore, specify prior distribution models assuming absence hierarchical structure adding priors_hierarchical_null = prior(distribution = \"spike\", parameters = list(\"location\" = 0)) argument. summarize resulting model ensemble find Hierarchical component longer equivalent Heterogeneity component – new model specification allowed us compare random effect models assuming presence hierarchical structure random effect models assuming absence hierarchical structure. resulting inclusion Bayes factor hierarchical structure shows extreme evidence favor clustering effect sizes, BFρρ‾=4624\\text{BF}_{\\rho\\bar{\\rho}} = 4624, .e., extreme evidence intervention results similar effects within districts.","code":"hierarchical_test <- RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_heterogeneity_null = NULL, priors_hierarchical_null = prior(distribution = \"spike\", parameters = list(\"location\" = 0)), priors_bias = NULL, parallel = TRUE, seed = 1) summary(hierarchical_test) #> Call: #> RoBMA(d = dat$yi, v = dat$vi, study_ids = dat$district, priors_bias = NULL, #> priors_heterogeneity_null = NULL, priors_hierarchical_null = prior(distribution = \"spike\", #> parameters = list(location = 0)), parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/4 0.500 0.478 0.917 #> Heterogeneity 4/4 1.000 1.000 Inf #> Hierarchical 2/4 0.500 1.000 4624.794 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.087 0.000 0.000 0.314 #> tau 0.326 0.317 0.231 0.472 #> rho 0.659 0.675 0.354 0.879 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"illustrated estimate hierarchical Bayesian model-averaged meta-analysis using RoBMA package. hBMA model allows us test presence vs absence effect heterogeneity simultaneously adjusting clustered effect size estimates. current implementation allows us draw fully Bayesian inference, incorporate prior information, acknowledge model uncertainty, limitations contrast metafor package. E.g., RoBMA package allows simple nested random effects (.e., estimates within studies, schools within districts etc). simple nesting allows us break full covariance matrix per cluster block matrices speeds already demanding computation. Furthermore, computational complexity significantly increases considering selection models need compute exponentially increasing number multivariate normal probabilities increasing cluster size (existence clusters four studies makes current implementation impractical due computational demands). However, current limitations end road, exploring approaches (e.g., specifying PET-PEESE style publication bias adjustment dependency adjustments) future vignette.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/HierarchicalBMA.html","id":"footnotes","dir":"Articles","previous_headings":"","what":"Footnotes","title":"Hierarchical Bayesian Model-Averaged Meta-Analysis","text":"1^1 also model-average across hierarchical structure assuming fixed effect models, .e., τ∼f(.)\\tau \\sim f(.) ρ=1\\rho = 1. However specifying model ensemble beyond scope vignette, see Custom ensembles vignette hints.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/articles/MedicineBiBMA.html","id":"binomial-normal-bayesian-model-averaged-meta-analysis","dir":"Articles","previous_headings":"","what":"Binomial-Normal Bayesian Model-Averaged Meta-Analysis","title":"Informed Bayesian Model-Averaged Meta-Analysis with Binary Outcomes","text":"illustrate fit binomial-normal Bayesian model-averaged meta-analysis using RoBMA R package. purpose, reproduce example adverse effects honey treating acute cough children Bartoš et al. (2023), reanalyzed two studies adverse events nervousness, insomnia, hyperactivity honey vs. treatment condition subjected meta-analysis Oduwole et al. (2018). load RoBMA package specify number adverse events sample sizes arm described p. 73 (Oduwole et al., 2018). Notice studies reported adverse events control group. Using normal-normal meta-analytic model log odds ratios require continuity correction, might result bias. Binomial-normal models allow us circumvent issue modeling observed proportions directly (see Bartoš et al. (2023) details). First, fit binomial-normal Bayesian model-averaged meta-analysis using informed prior distributions based Acute Respiratory Infections subfield. use BiBMA function specify observed events (x1 x2) sample size (n1 n2) adverse events sample sizes arm. use prior_informed function specify informed prior distributions individual medical subfields automatically. priors_effect priors_heterogeneity corresponding μ∼T(0,0.48,3)\\mu \\sim T(0,0.48,3) τ∼InvGamma(1.67,0.45)\\tau \\sim InvGamma(1.67, 0.45) prior distributions (see ?prior_informed details regarding informed prior distributions). obtain output summary function. Adding conditional = TRUE argument allows us inspect conditional estimates, .e., effect size estimate assuming models specifying presence effect true, heterogeneity estimates assuming models specifying presence heterogeneity true. also set output_scale = \"\" argument display effect size estimates odds ratio scale. output summary.RoBMA() function three parts. first part, ‘Robust Bayesian Meta-Analysis’ heading provides basic summary fitted models component types (presence Effect Heterogeneity). results show inclusion Bayes factor effect corresponds one reported Bartoš et al. (2023), BF10=2.63\\text{BF}_{10} = 2.63 BFrf=1.30\\text{BF}_{\\text{rf}} = 1.30 (MCMC error)—weak/undecided evidence presence effect heterogeneity. second part, ‘Model-averaged estimates’ heading displays parameter estimates model-averaged across specified models (.e., including models specifying effect size zero). estimates shrunk towards null hypotheses null effect heterogeneity accordance posterior uncertainty presence effect heterogeneity. find model-averaged mean effect = 3.39, 95% CI [0.84, 15.14], heterogeneity estimate τlogOR=0.42\\tau_\\text{logOR} = 0.42, 95% CI [0.00, 2.59]. third part, ‘Conditional estimates’ heading displays conditional effect size heterogeneity estimates (.e., estimates assuming presence effect heterogeneity) corresponding one reported Bartoš et al. (2023), = 4.24, 95% CI [0.78, 17.61], heterogeneity estimate τlogOR=0.75\\tau_\\text{logOR} = 0.75, 95% CI [0.10, 3.23]. can also visualize posterior distributions effect size heterogeneity parameters using plot() function. , set conditional = TRUE argument display conditional effect size estimate prior = TRUE include prior distribution plot. Additional visualizations summaries demonstrated Reproducing BMA Informed Bayesian Model-Averaged Meta-Analysis Medicine vignettes.","code":"library(RoBMA) events_experimental <- c(5, 2) events_control <- c(0, 0) observations_experimental <- c(35, 40) observations_control <- c(39, 40) study_names <- c(\"Paul 2007\", \"Shadkam 2010\") fit <- BiBMA( x1 = events_experimental, x2 = events_control, n1 = observations_experimental, n2 = observations_control, study_names = study_names, priors_effect = prior_informed(\"Acute Respiratory Infections\", type = \"logOR\", parameter = \"effect\"), priors_heterogeneity = prior_informed(\"Acute Respiratory Infections\", type = \"logOR\", parameter = \"heterogeneity\"), seed = 1 ) summary(fit, conditional = TRUE, output_scale = \"OR\") #> Call: #> BiBMA(x1 = events_experimental, x2 = events_control, n1 = observations_experimental, #> n2 = observations_control, study_names = study_names, priors_effect = prior_informed(\"Acute Respiratory Infections\", #> type = \"logOR\", parameter = \"effect\"), priors_heterogeneity = prior_informed(\"Acute Respiratory Infections\", #> type = \"logOR\", parameter = \"heterogeneity\"), seed = 1) #> #> Bayesian model-averaged meta-analysis (binomial-normal model) #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/4 0.500 0.725 2.630 #> Heterogeneity 2/4 0.500 0.564 1.296 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 3.389 1.642 0.842 15.143 #> tau 0.420 0.158 0.000 2.594 #> The effect size estimates are summarized on the OR scale and heterogeneity is summarized on the logOR scale (priors were specified on the log(OR) scale). #> #> Conditional estimates: #> Mean Median 0.025 0.975 #> mu 4.242 2.261 0.781 17.613 #> tau 0.747 0.426 0.097 3.233 #> The effect size estimates are summarized on the OR scale and heterogeneity is summarized on the logOR scale (priors were specified on the log(OR) scale). plot(fit, parameter = \"mu\", prior = TRUE, conditional = TRUE)"},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/articles/MedicineBMA.html","id":"reproducing-informed-bayesian-model-averaged-meta-analysis-bma","dir":"Articles","previous_headings":"","what":"Reproducing Informed Bayesian Model-Averaged Meta-Analysis (BMA)","title":"Informed Bayesian Model-Averaged Meta-Analysis in Medicine","text":"illustrate fit informed BMA (adjusting publication bias) using RoBMA R package. purpose, reproduce dentine hypersensitivity example Bartoš et al. (2021), reanalyzed five studies tactile outcome assessment subjected meta-analysis Poulsen et al. (2006). load dentine hypersensitivity data included package. reproduce analysis example, need set informed empirical prior distributions effect sizes (μ\\mu) heterogeneity (τ\\tau) parameters Bartoš et al. (2021) obtained Cochrane database systematic reviews. can either set manually, priors_effect priors_heterogeneity corresponding δ∼T(0,0.51,5)\\delta \\sim T(0,0.51,5) τ∼InvGamma(1.79,0.28)\\tau \\sim InvGamma(1.79,0.28) informed prior distributions “oral health” subfield removing publication bias adjustment models setting priors_bias = NULL1^1. Note package contains function NoBMA() version 3.1 skips publication bias adjustment directly. Alternatively, can utilize prior_informed function prepares informed prior distributions individual medical subfields automatically. name argument specifies medical subfield name (use print(BayesTools::prior_informed_medicine_names) check names available subfields). parameter argument specifies whether want prior distribution effect size heterogeneity. Finally, type argument specifies type measure use meta-analysis (see ?prior_informed details regarding informed prior distributions). obtain output summary function. Adding conditional = TRUE argument allows us inspect conditional estimates, .e., effect size estimate assuming models specifying presence effect true heterogeneity estimates assuming models specifying presence heterogeneity true2^2. output summary.RoBMA() function 3 parts. first one ‘Robust Bayesian Meta-Analysis’ heading provides basic summary fitted models component types (presence Effect Heterogeneity). table summarizes prior posterior probabilities inclusion Bayes factors individual components. results show inclusion Bayes factor effect corresponds one reported Bartoš et al. (2021), BF10=218.53\\text{BF}_{10} = 218.53 BFrf=3.52\\text{BF}_{\\text{rf}} = 3.52 (MCMC error). second part ‘Model-averaged estimates’ heading displays parameter estimates model-averaged across specified models (.e., including models specifying effect size zero). ignore section move last part. third part ‘Conditional estimates’ heading displays conditional effect size estimate corresponding one reported Bartoš et al. (2021), δ=1.082\\delta = 1.082, 95% CI [0.686,1.412], heterogeneity estimate (reported previously).","code":"library(RoBMA) data(\"Poulsen2006\", package = \"RoBMA\") Poulsen2006 #> d se study #> 1 0.9073050 0.2720456 STD-Schiff-1994 #> 2 0.7207589 0.1977769 STD-Silverman-1996 #> 3 1.3305829 0.2721648 STD-Sowinski-2000 #> 4 1.7688872 0.2656483 STD-Schiff-2000-(2) #> 5 1.3286828 0.3582617 STD-Schiff-1998 fit_BMA <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, priors_effect = prior(distribution = \"t\", parameters = list(location = 0, scale = 0.51, df = 5)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1.79, scale = 0.28)), priors_bias = NULL, transformation = \"cohens_d\", seed = 1, parallel = TRUE) fit_BMA <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, priors_effect = prior_informed(name = \"oral health\", parameter = \"effect\", type = \"smd\"), priors_heterogeneity = prior_informed(name = \"oral health\", parameter = \"heterogeneity\", type = \"smd\"), priors_bias = NULL, transformation = \"cohens_d\", seed = 1, parallel = TRUE) summary(fit_BMA, conditional = TRUE) #> Call: #> RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, #> transformation = \"cohens_d\", priors_effect = prior_informed(name = \"oral health\", #> parameter = \"effect\", type = \"smd\"), priors_heterogeneity = prior_informed(name = \"oral health\", #> parameter = \"heterogeneity\", type = \"smd\"), priors_bias = NULL, #> parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/4 0.500 0.995 217.517 #> Heterogeneity 2/4 0.500 0.778 3.511 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 1.076 1.088 0.664 1.422 #> tau 0.231 0.208 0.000 0.726 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). #> #> Conditional estimates: #> Mean Median 0.025 0.975 #> mu 1.082 1.090 0.701 1.422 #> tau 0.297 0.255 0.075 0.779 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."},{"path":"https://https://fbartos.github.io/RoBMA/articles/MedicineBMA.html","id":"visualizing-the-results","dir":"Articles","previous_headings":"","what":"Visualizing the Results","title":"Informed Bayesian Model-Averaged Meta-Analysis in Medicine","text":"RoBMA package provides extensive options visualizing results. , visualize prior (grey) posterior (black) distribution mean parameter. default, function plots model-averaged estimates across models; arrows represent probability spike, lines represent posterior density models assuming non-zero effect. secondary y-axis (right) shows probability spike (value 0) decreasing 0.50, 0.005 (also obtainable ‘Robust Bayesian Meta-Analysis’ field summary.RoBMA() function). visualize conditional effect size estimate, can add conditional = TRUE argument, displays model-averaged posterior distribution effect size parameter models assuming presence effect. can also visualize estimates individual models used ensemble. plot_models() function, visualizes effect size estimates 95% CI specified model included ensemble. Model 1 corresponds fixed effect model assuming absence effect, H0fH_0^{\\text{f}}, Model 2 corresponds random effect model assuming absence effect, H0rH_0^{\\text{r}}, Model 3 corresponds fixed effect model assuming presence effect, H1fH_1^{\\text{f}}, Model 4 corresponds random effect model assuming presence effect, H1rH_1^{\\text{r}}). size square representing mean estimate reflects posterior model probability model, also displayed right-hand side panel. bottom part figure shows model averaged-estimate combination individual model posterior distributions weighted posterior model probabilities. see posterior model probability first two models decreased essentially zero (rounding two decimals), completely omitting estimates figure. Furthermore, much larger box Model 4 (random effect model assuming presence effect) shows Model 4 received largest share posterior probability, P(H1r)=0.77P(H_1^{\\text{r}}) = 0.77) last type visualization show forest plot. displays original studies’ effects meta-analytic estimate within one figure. can requested using forest() function. , set conditional = TRUE argument display conditional model-averaged effect size estimate bottom. options provided plotting function, see documentation using ?plot.RoBMA(), ?plot_models(), ?forest().","code":"plot(fit_BMA, parameter = \"mu\", prior = TRUE) plot(fit_BMA, parameter = \"mu\", prior = TRUE, conditional = TRUE) plot_models(fit_BMA) forest(fit_BMA, conditional = TRUE)"},{"path":"https://https://fbartos.github.io/RoBMA/articles/MedicineBMA.html","id":"adjusting-for-publication-bias-with-robust-bayesian-meta-analysis","dir":"Articles","previous_headings":"","what":"Adjusting for Publication Bias with Robust Bayesian Meta-Analysis","title":"Informed Bayesian Model-Averaged Meta-Analysis in Medicine","text":"Finally, illustrate adjust informed BMA publication bias robust Bayesian meta-analysis Maier et al. (2023). short, specify additional models assuming presence publication bias correcting either specifying selection model operating pp-values (Vevea & Hedges, 1995) specifying publication bias adjustment method correcting relationship effect sizes standard errors – PET-PEESE (Stanley, 2017; Stanley & Doucouliagos, 2014). See Bartoš et al. (2022) tutorial. obtain proper publication bias adjustment comparison, fit informed BMA model using default effect size transformation (Fisher’s zz). obtain noticeably stronger evidence presence effect. result placing weights fixed-effect models, especially fixed-effect model assuming presence effect H1fH_1^f. case, increase posterior model probability H1fH_1^f occurred model predicted data slightly better removing correlation effect sizes standard errors (consequence using Fisher’s zz transformation). Nevertheless, conditional effect size estimate stayed almost . Now, fit publication bias-adjusted model simply removing priors_bias = NULL argument, allows us obtain default 36 models ensemble called RoBMA-PSMA (Bartoš et al., 2023). notice additional values ‘Components summary’ table ‘Bias’ row. model now extended 32 publication bias adjustment models account 50% prior model probability. comparing RoBMA second BMA fit, notice large decrease inclusion Bayes factor presence effect BF10=6.02\\text{BF}_{10} = 6.02 vs. BF10=347.93\\text{BF}_{10} = 347.93, still, however, presents moderate evidence presence effect. can quantify evidence favor publication bias inclusion Bayes factor publication bias BFpb=2.30\\text{BF}_{pb} = 2.30, can interpreted weak evidence favor publication bias. can also compare publication bias unadjusted publication bias-adjusted conditional effect size estimates. Including models assuming publication bias model-averaged estimate (assuming presence effect) slightly decreases estimated effect δ=0.838\\delta = 0.838, 95% CI [-0.035, 1.297] much wider confidence interval, visualized prior posterior conditional effect size estimate plot.","code":"fit_BMAb <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, priors_effect = prior_informed(name = \"oral health\", parameter = \"effect\", type = \"smd\"), priors_heterogeneity = prior_informed(name = \"oral health\", parameter = \"heterogeneity\", type = \"smd\"), priors_bias = NULL, seed = 1, parallel = TRUE) summary(fit_BMAb, conditional = TRUE) #> Call: #> RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, #> priors_effect = prior_informed(name = \"oral health\", parameter = \"effect\", #> type = \"smd\"), priors_heterogeneity = prior_informed(name = \"oral health\", #> parameter = \"heterogeneity\", type = \"smd\"), priors_bias = NULL, #> parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/4 0.500 0.997 347.932 #> Heterogeneity 2/4 0.500 0.723 2.608 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 1.045 1.052 0.705 1.344 #> tau 0.186 0.163 0.000 0.623 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). #> #> Conditional estimates: #> Mean Median 0.025 0.975 #> mu 1.048 1.053 0.720 1.344 #> tau 0.256 0.220 0.064 0.681 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). fit_RoBMA <- RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, priors_effect = prior_informed(name = \"oral health\", parameter = \"effect\", type = \"smd\"), priors_heterogeneity = prior_informed(name = \"oral health\", parameter = \"heterogeneity\", type = \"smd\"), seed = 1, parallel = TRUE) summary(fit_RoBMA, conditional = TRUE) #> Call: #> RoBMA(d = Poulsen2006$d, se = Poulsen2006$se, study_names = Poulsen2006$study, #> priors_effect = prior_informed(name = \"oral health\", parameter = \"effect\", #> type = \"smd\"), priors_heterogeneity = prior_informed(name = \"oral health\", #> parameter = \"heterogeneity\", type = \"smd\"), parallel = TRUE, #> seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 18/36 0.500 0.858 6.022 #> Heterogeneity 18/36 0.500 0.714 2.502 #> Bias 32/36 0.500 0.697 2.304 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.722 0.880 0.000 1.283 #> tau 0.202 0.161 0.000 0.799 #> omega[0,0.025] 1.000 1.000 1.000 1.000 #> omega[0.025,0.05] 0.943 1.000 0.329 1.000 #> omega[0.05,0.5] 0.874 1.000 0.071 1.000 #> omega[0.5,0.95] 0.855 1.000 0.042 1.000 #> omega[0.95,0.975] 0.866 1.000 0.050 1.000 #> omega[0.975,1] 0.897 1.000 0.057 1.000 #> PET 0.931 0.000 0.000 4.927 #> PEESE 1.131 0.000 0.000 12.261 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). #> (Estimated publication weights omega correspond to one-sided p-values.) #> #> Conditional estimates: #> Mean Median 0.025 0.975 #> mu 0.838 0.938 -0.035 1.297 #> tau 0.285 0.227 0.064 0.906 #> omega[0,0.025] 1.000 1.000 1.000 1.000 #> omega[0.025,0.05] 0.736 0.829 0.092 1.000 #> omega[0.05,0.5] 0.411 0.373 0.014 0.951 #> omega[0.5,0.95] 0.320 0.249 0.008 0.919 #> omega[0.95,0.975] 0.376 0.311 0.009 0.958 #> omega[0.975,1] 0.518 0.425 0.010 1.000 #> PET 2.909 3.136 0.171 5.614 #> PEESE 7.048 6.034 0.375 18.162 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). #> (Estimated publication weights omega correspond to one-sided p-values.) plot(fit_RoBMA, parameter = \"mu\", prior = TRUE, conditional = TRUE)"},{"path":"https://https://fbartos.github.io/RoBMA/articles/MedicineBMA.html","id":"footnotes","dir":"Articles","previous_headings":"","what":"Footnotes","title":"Informed Bayesian Model-Averaged Meta-Analysis in Medicine","text":"1^1 additional setting transformation = \"cohens_d\" allows us get comparable results metaBMA R package since RoBMA otherwise internally transforms effect sizes Fisher’s zz fitting purposes. seed = 1 parallel = TRUE options grant us exact reproducibility results parallelization fitting process. 2^2 model-averaged estimates RoBMA returns default model-averaged across specified models – different behavior metaBMA package default returns call “conditional” estimates RoBMA.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/articles/MetaRegression.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Robust Bayesian Model-Averaged Meta-Regression","text":"start loading Andrews2021 dataset included RoBMA R package, contains 36 estimates effect household chaos child executive functions mean age assessment type covariates. dataset includes correlation coefficients (r), standard errors correlation coefficients (se), type executive function assessment (measure), mean age children (age) study.","code":"library(RoBMA) data(\"Andrews2021\", package = \"RoBMA\") head(Andrews2021) #> r se measure age #> 1 0.070 0.04743416 direct 4.606660 #> 2 0.033 0.04371499 direct 2.480833 #> 3 0.170 0.10583005 direct 7.750000 #> 4 0.208 0.08661986 direct 4.000000 #> 5 0.270 0.02641969 direct 4.000000 #> 6 0.170 0.05147815 direct 4.487500"},{"path":"https://https://fbartos.github.io/RoBMA/articles/MetaRegression.html","id":"frequentist-meta-regression","dir":"Articles","previous_headings":"","what":"Frequentist Meta-Regression","title":"Robust Bayesian Model-Averaged Meta-Regression","text":"start fitting frequentist meta-regression using metafor R package (Wolfgang, 2010). Andrews et al. (2021) estimated univariate meta-regressions moderator, directly proceed analyzing moderators simultaneously. consistency original reporting, estimate meta-regression using correlation coefficients standard errors provided (Andrews et al., 2021); however, note Fisher’s z transformation recommended estimating meta-analytic models (e.g., Stanley et al. (2024)). results reveal statistically significant moderation effect executive function assessment type effect household chaos child executive functions (p=0.0099p = 0.0099). explore moderation effect , estimate estimated marginal means executive function assessment type using emmeans R package (Lenth et al., 2017). Studies using informant-completed questionnaires show stronger effect household chaos child executive functions, r = 0.229, 95% CI [0.161, 0.297], direct assessment, r = 0.109, 95% CI [0.049, 0.169]; types studies show statistically significant effects. mean age children significantly moderate effect (p=0.627p = 0.627) estimated regression coefficient b = 0.003, 95% CI [-0.009, 0.015]. usual, frequentist inference limits us failing reject null hypothesis. , try overcome limitation Bayesian model-averaged meta-regression.","code":"fit_rma <- metafor::rma(yi = r, sei = se, mods = ~ measure + age, data = Andrews2021) fit_rma #> #> Mixed-Effects Model (k = 36; tau^2 estimator: REML) #> #> tau^2 (estimated amount of residual heterogeneity): 0.0150 (SE = 0.0045) #> tau (square root of estimated tau^2 value): 0.1226 #> I^2 (residual heterogeneity / unaccounted variability): 91.28% #> H^2 (unaccounted variability / sampling variability): 11.47 #> R^2 (amount of heterogeneity accounted for): 15.24% #> #> Test for Residual Heterogeneity: #> QE(df = 33) = 340.7613, p-val < .0001 #> #> Test of Moderators (coefficients 2:3): #> QM(df = 2) = 7.5445, p-val = 0.0230 #> #> Model Results: #> #> estimate se zval pval ci.lb ci.ub #> intrcpt 0.0898 0.0467 1.9232 0.0545 -0.0017 0.1813 . #> measureinformant 0.1202 0.0466 2.5806 0.0099 0.0289 0.2115 ** #> age 0.0030 0.0062 0.4867 0.6265 -0.0091 0.0151 #> #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 emmeans::emmeans(metafor::emmprep(fit_rma), specs = \"measure\") #> measure emmean SE df asymp.LCL asymp.UCL #> direct 0.109 0.0305 Inf 0.0492 0.169 #> informant 0.229 0.0347 Inf 0.1612 0.297 #> #> Confidence level used: 0.95"},{"path":"https://https://fbartos.github.io/RoBMA/articles/MetaRegression.html","id":"bayesian-meta-regression-specification","dir":"Articles","previous_headings":"","what":"Bayesian Meta-Regression Specification","title":"Robust Bayesian Model-Averaged Meta-Regression","text":"proceed Bayesian model-averaged meta-regression, provide quick overview regression model specification. contrast frequentist meta-regression, need specify prior distributions regression coefficients, encode tested hypotheses presence vs. absence moderation (specifying different prior distributions corresponds different hypotheses results different conclusions). Importantly, treatment continuous categorical covariates differs Bayesian model-averaged meta-regression.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/MetaRegression.html","id":"continuous-vs--categorical-moderators-and-default-prior-distributions","dir":"Articles","previous_headings":"Bayesian Meta-Regression Specification","what":"Continuous vs. Categorical Moderators and Default Prior Distributions","title":"Robust Bayesian Model-Averaged Meta-Regression","text":"default prior distribution continuous moderators normal prior distribution mean 0 standard deviation 1/4. words, default prior distribution assumes effect moderator small smaller moderation effects likely larger effects. default choice continuous moderators can overridden prior_covariates argument (continuous covariates) priors argument (specific covariates, see ?RoBMA.reg information). package automatically standardizes continuous moderators. achieves scale-invariance specified prior distributions ensures prior distribution intercept correspond grand mean effect. setting can overridden specifying standardize_predictors = FALSE argument. default prior distribution categorical moderators normal distribution mean 0 standard deviation 1/4, representing deviation level grand mean effect. package uses standardized orthonormal contrasts (contrast = \"meandif\") model deviations category grand mean effect. default choice categorical moderators can overridden prior_factors argument (categorical covariates) priors argument (specific covariates, see ?RoBMA.reg information). \"meandif\" contrasts achieve label invariance (.e., coding categorical covariates affect results) prior distribution intercept corresponds grand mean effect. Alternatively, package also allows specifying \"treatment\" contrasts, result prior distribution difference default level remaining levels categorical covariate (intercept corresponding effect default factor level).","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/MetaRegression.html","id":"effect-size-input-specification","dir":"Articles","previous_headings":"Bayesian Meta-Regression Specification","what":"Effect Size Input Specification","title":"Robust Bayesian Model-Averaged Meta-Regression","text":"Prior distributions Bayesian meta-analyses calibrated standardized effect size measures. , fitting function needs know kind effect size supplied input. RoBMA() function, achieved d, r, logOR, , z, se, v, n, lCI, uCI arguments. input passed combine_data() function background combines effect sizes merges single data.frame. RoBMA.reg() (NoBMA.reg()) function requires dataset passed data.frame (without missing values) column names identifying - moderators passed using formula interface (.e., ~ measure + age example) - effect sizes standard errors (.e., r se example). , crucial column names correctly identify standardized effect sizes, standard errors, sample sizes, moderators.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/MetaRegression.html","id":"bayesian-model-averaged-meta-regression","dir":"Articles","previous_headings":"","what":"Bayesian Model-Averaged Meta-Regression","title":"Robust Bayesian Model-Averaged Meta-Regression","text":"fit Bayesian model-averaged meta-regression using NoBMA.reg() function (NoBMA.reg() function wrapper around RoBMA.reg() function automatically removes models adjusting publication bias). specify model formula ~ operator similarly rma() function pass dataset data.frame named columns outlined section (names need identify moderators effect size measures). also set parallel = TRUE argument speed computation running chains parallel seed = 1 argument ensure reproducibility. Note NoBMA.reg() function specifies combination models assuming presence vs. absence effect, heterogeneity, moderation measure, moderation age, corresponds 2*2*2*2=162*2*2*2=16 models. Including additional moderator doubles number models, leading exponential increase model count significantly longer fitting times. ensemble estimated, can use summary() functions output_scale = \"r\" argument, produces meta-analytic estimates transformed correlation scale. summary function produces output multiple sections first section contains Components summary hypothesis test results overall effect size heterogeneity. find overwhelming evidence inclusion Bayes factors (Inclusion BF) 10,000. second section contains Meta-regression components summary hypothesis test results moderators. find moderate evidence moderation executive function assessment type, BFmeasure=4.74\\text{BF}_{\\text{measure}} = 4.74. Furthermore, find moderate evidence null hypothesis moderation mean age children, BFage=0.245\\text{BF}_{\\text{age}} = 0.245 (.e., BF null 1/0.245=4.081/0.245 = 4.08). findings extend frequentist meta-regression disentangling absence evidence evidence absence. third section contains Model-averaged estimates model-averaged estimates mean effect ρ=0.16\\rho = 0.16, 95% CI [0.12, 0.21] -study heterogeneity τFisher’s z=0.12\\tau_{\\text{Fisher's z}} = 0.12, 95% CI [0.09, 0.17]. fourth section contains Model-averaged meta-regression estimates model-averaged regression coefficient estimates. main difference usual frequentist meta-regression output categorical predictors summarized difference grand mean factor level. , intercept regression coefficient estimate corresponds grand mean effect measure [dif: direct] regression coefficient estimate -0.047, 95% CI [-0.099, 0.000] corresponds difference direct assessment grand mean. , results suggest effect size studies using direct assessment lower comparison grand mean studies. age regression coefficient estimate standardized, therefore, increase 0.003, 95% CI [-0.011, 0.043] corresponds increase mean effect increasing mean age children one standard deviation. Similarly frequentist meta-regression, can use marginal_summary() function obtain marginal estimates factor levels. estimated marginal means similar frequentist results. Studies using informant-completed questionnaires show stronger effect household chaos child executive functions, ρ=0.208\\rho = 0.208, 95% CI [0.130, 0.280], direct assessment, ρ=0.117\\rho = 0.117, 95% CI [0.052, 0.185]. last column summarizes results test null hypothesis marginal means equals 0. , find strong evidence effect size studies using informant-completed questionnaires differing zero, BF10=50.1\\text{BF}_{10} = 50.1 extreme evidence effect size studies using direct assessment differing zero, BF10=∞\\text{BF}_{10} = \\infty. test performed using change prior posterior distribution 0 (.e., Savage-Dickey density ratio) assuming presence overall effect presence difference according tested factor. tests use prior posterior samples, calculating Bayes factor can problematic posterior distribution far tested value. cases, warning messages printed BF10=∞\\text{BF}_{10} = \\infty returned (like )—actual Bayes factor less infinity, still large computed precisely given posterior samples. full model-averaged posterior marginal means distribution can visualized marginal_plot() function.","code":"fit_BMA <- NoBMA.reg(~ measure + age, data = Andrews2021, parallel = TRUE, seed = 1) summary(fit_BMA, output_scale = \"r\") #> Call: #> RoBMA.reg(formula = formula, data = data, test_predictors = test_predictors, #> study_names = study_names, study_ids = study_ids, transformation = transformation, #> prior_scale = prior_scale, standardize_predictors = standardize_predictors, #> effect_direction = \"positive\", priors = priors, model_type = model_type, #> priors_effect = priors_effect, priors_heterogeneity = priors_heterogeneity, #> priors_bias = NULL, priors_effect_null = priors_effect_null, #> priors_heterogeneity_null = priors_heterogeneity_null, priors_bias_null = prior_none(), #> priors_hierarchical = priors_hierarchical, priors_hierarchical_null = priors_hierarchical_null, #> prior_covariates = prior_covariates, prior_covariates_null = prior_covariates_null, #> prior_factors = prior_factors, prior_factors_null = prior_factors_null, #> chains = chains, sample = sample, burnin = burnin, adapt = adapt, #> thin = thin, parallel = parallel, autofit = autofit, autofit_control = autofit_control, #> convergence_checks = convergence_checks, save = save, seed = seed, #> silent = silent) #> #> Bayesian model-averaged meta-regression (normal-normal model) #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 8/16 0.500 1.000 6.637645e+05 #> Heterogeneity 8/16 0.500 1.000 3.439130e+40 #> #> Meta-regression components summary: #> Models Prior prob. Post. prob. Inclusion BF #> measure 8/16 0.500 0.826 4.739 #> age 8/16 0.500 0.197 0.245 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.163 0.163 0.118 0.208 #> tau 0.121 0.120 0.086 0.167 #> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale). #> #> Model-averaged meta-regression estimates: #> Mean Median 0.025 0.975 #> intercept 0.163 0.163 0.118 0.208 #> measure [dif: direct] -0.047 -0.051 -0.099 0.000 #> measure [dif: informant] 0.047 0.051 0.000 0.099 #> age 0.003 0.000 -0.011 0.043 #> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale). marginal_summary(fit_BMA, output_scale = \"r\") #> Call: #> RoBMA.reg(formula = formula, data = data, test_predictors = test_predictors, #> study_names = study_names, study_ids = study_ids, transformation = transformation, #> prior_scale = prior_scale, standardize_predictors = standardize_predictors, #> effect_direction = \"positive\", priors = priors, model_type = model_type, #> priors_effect = priors_effect, priors_heterogeneity = priors_heterogeneity, #> priors_bias = NULL, priors_effect_null = priors_effect_null, #> priors_heterogeneity_null = priors_heterogeneity_null, priors_bias_null = prior_none(), #> priors_hierarchical = priors_hierarchical, priors_hierarchical_null = priors_hierarchical_null, #> prior_covariates = prior_covariates, prior_covariates_null = prior_covariates_null, #> prior_factors = prior_factors, prior_factors_null = prior_factors_null, #> chains = chains, sample = sample, burnin = burnin, adapt = adapt, #> thin = thin, parallel = parallel, autofit = autofit, autofit_control = autofit_control, #> convergence_checks = convergence_checks, save = save, seed = seed, #> silent = silent) #> #> Robust Bayesian meta-analysis #> Model-averaged marginal estimates: #> Mean Median 0.025 0.975 Inclusion BF #> intercept 0.163 0.163 0.118 0.208 Inf #> measure[direct] 0.117 0.116 0.052 0.185 50.151 #> measure[informant] 0.208 0.210 0.130 0.280 Inf #> age[-1SD] 0.160 0.161 0.106 0.208 Inf #> age[0SD] 0.163 0.163 0.118 0.208 Inf #> age[1SD] 0.166 0.165 0.117 0.220 Inf #> The estimates are summarized on the correlation scale (priors were specified on the Cohen's d scale). #> mu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. #> mu_measure[informant]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. #> mu_age[-1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. #> mu_age[0SD]: There is a considerable cluster of prior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid. #> mu_age[0SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. #> mu_age[1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. marginal_plot(fit_BMA, parameter = \"measure\", output_scale = \"r\", lwd = 2)"},{"path":"https://https://fbartos.github.io/RoBMA/articles/MetaRegression.html","id":"robust-bayesian-model-averaged-meta-regression","dir":"Articles","previous_headings":"","what":"Robust Bayesian Model-Averaged Meta-Regression","title":"Robust Bayesian Model-Averaged Meta-Regression","text":"Finally, adjust Bayesian model-averaged meta-regression model fitting robust Bayesian model-averaged meta-regression. contrast previous publication bias unadjusted model ensemble, RoBMA-reg extends model ensemble publication bias component specified via 6 weight functions PET-PEESE (Bartoš, Maier, Wagenmakers, et al., 2023). use RoBMA.reg() function arguments previous section. estimation time increases ensemble now contains 144 models. previously described functions manipulating fitted model work identically publication bias adjusted model. , just briefly mention main differences found adjusting publication bias. RoBMA-reg reveals strong evidence publication bias BFpb=28.0\\text{BF}_{\\text{pb}} = 28.0. Furthermore, accounting publication bias turns previously found evidence overall effect weak evidence effect BF10=0.50\\text{BF}_{10} = 0.50 notably reduces mean effect estimate ρ=0.031\\rho = 0.031, 95% CI [0.000, 0.164]. estimated marginal means now suggest studies using informant-completed questionnaires show much smaller effect household chaos child executive functions, ρ=0.093\\rho = 0.093, 95% CI [0.000, 0.223] moderate evidence effect, BF10=7.64\\text{BF}_{10} = 7.64, studies using direct assessment even provide weak evidence effect household chaos child executive functions, BF10=0.58\\text{BF}_{10} = 0.58, likely effect sizes around zero, ρ=−0.031\\rho = -0.031, 95% CI [-0.105, 0.121]. visual summary estimated marginal means highlights considerably wider model-averaged posterior distributions marginal means—consequence accounting adjusting publication bias. Bayesian model-averaged meta-regression models compatible remaining custom specification, visualization, summary functions included RoBMA R package, highlighted vignettes. E.g., custom model specification demonstrated vignette Fitting Custom Meta-Analytic Ensembles visualizations summaries demonstrated Reproducing BMA Informed Bayesian Model-Averaged Meta-Analysis Medicine vignettes.","code":"fit_RoBMA <- RoBMA.reg(~ measure + age, data = Andrews2021, parallel = TRUE, seed = 1) summary(fit_RoBMA, output_scale = \"r\") #> Call: #> RoBMA.reg(formula = ~measure + age, data = Andrews2021, chains = 1, #> parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-regression #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 72/144 0.500 0.334 5.020000e-01 #> Heterogeneity 72/144 0.500 1.000 1.043816e+23 #> Bias 128/144 0.500 0.965 2.795800e+01 #> #> Meta-regression components summary: #> Models Prior prob. Post. prob. Inclusion BF #> measure 72/144 0.500 0.950 19.086 #> age 72/144 0.500 0.154 0.182 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.031 0.000 0.000 0.164 #> tau 0.106 0.104 0.074 0.147 #> omega[0,0.025] 1.000 1.000 1.000 1.000 #> omega[0.025,0.05] 0.999 1.000 1.000 1.000 #> omega[0.05,0.5] 0.998 1.000 1.000 1.000 #> omega[0.5,0.95] 0.997 1.000 1.000 1.000 #> omega[0.95,0.975] 0.997 1.000 1.000 1.000 #> omega[0.975,1] 0.997 1.000 1.000 1.000 #> PET 2.056 2.494 0.000 3.293 #> PEESE 1.916 0.000 0.000 19.068 #> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale). #> (Estimated publication weights omega correspond to one-sided p-values.) #> #> Model-averaged meta-regression estimates: #> Mean Median 0.025 0.975 #> intercept 0.031 0.000 0.000 0.164 #> measure [dif: direct] -0.063 -0.064 -0.106 0.000 #> measure [dif: informant] 0.063 0.064 0.000 0.106 #> age 0.000 0.000 -0.024 0.022 #> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale). marginal_summary(fit_RoBMA, output_scale = \"r\") #> Call: #> RoBMA.reg(formula = ~measure + age, data = Andrews2021, chains = 1, #> parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Model-averaged marginal estimates: #> Mean Median 0.025 0.975 Inclusion BF #> intercept 0.031 0.000 0.000 0.164 0.516 #> measure[direct] -0.031 -0.056 -0.105 0.121 0.575 #> measure[informant] 0.093 0.077 0.000 0.223 7.643 #> age[-1SD] 0.031 0.000 -0.015 0.163 0.732 #> age[0SD] 0.031 0.000 0.000 0.164 1.013 #> age[1SD] 0.031 0.000 -0.024 0.168 0.743 #> The estimates are summarized on the correlation scale (priors were specified on the Cohen's d scale). #> mu_age[0SD]: There is a considerable cluster of prior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid. marginal_plot(fit_RoBMA, parameter = \"measure\", output_scale = \"r\", lwd = 2)"},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/articles/ReproducingBMA.html","id":"reproducing-bayesian-model-averaged-meta-analysis-bma","dir":"Articles","previous_headings":"","what":"Reproducing Bayesian Model-Averaged Meta-Analysis (BMA)","title":"Reproducing Bayesian Model-Averaged Meta-Analysis","text":"illustrate fit classical BMA (adjusting publication bias) using RoBMA. purpose, reproduce meta-analysis registered reports Power posing Gronau et al. (2017). focus analysis reported results using Cauchy prior distribution scale 1/21/\\sqrt{2} effect size estimation (half-Cauchy testing) inverse-gamma distribution shape = 1 scale = 0.15 heterogeneity parameter. can find figure original publication paper’s supplementary materials https://osf.io/fxg32/. First, load power posing data provided within metaBMA package reproduce analysis performed Gronau et al. (2017). output, can see inclusion Bayes factor effect size BF10=33.14BF_{10} = 33.14 effect size estimate 0.22, 95% HDI [0.09, 0.34], matches reported results. Please note metaBMA package model-averages across H1H_{1} models, whereas RoBMA package model-averages across models (assuming presence absence effect).","code":"data(\"power_pose\", package = \"metaBMA\") power_pose[,c(\"study\", \"effectSize\", \"SE\")] #> study effectSize SE #> 1 Bailey et al. 0.2507640 0.2071399 #> 2 Ronay et al. 0.2275180 0.1931046 #> 3 Klaschinski et al. 0.3186069 0.1423228 #> 4 Bombari et al. 0.2832082 0.1421356 #> 5 Latu et al. 0.1463949 0.1416107 #> 6 Keller et al. 0.1509773 0.1221166 fit_BMA_test <- metaBMA::meta_bma(y = power_pose$effectSize, SE = power_pose$SE, d = metaBMA::prior(family = \"halfcauchy\", param = 1/sqrt(2)), tau = metaBMA::prior(family = \"invgamma\", param = c(1, .15))) fit_BMA_est <- metaBMA::meta_bma(y = power_pose$effectSize, SE = power_pose$SE, d = metaBMA::prior(family = \"cauchy\", param = c(0, 1/sqrt(2))), tau = metaBMA::prior(family = \"invgamma\", param = c(1, .15))) fit_BMA_test$inclusion #> ### Inclusion Bayes factor ### #> Model Prior Posterior included #> 1 fixed_H0 0.25 0.00868 #> 2 fixed_H1 0.25 0.77745 x #> 3 random_H0 0.25 0.02061 #> 4 random_H1 0.25 0.19325 x #> #> Inclusion posterior probability: 0.971 #> Inclusion Bayes factor: 33.136 round(fit_BMA_est$estimates,2) #> mean sd 2.5% 50% 97.5% hpd95_lower hpd95_upper n_eff Rhat #> averaged 0.22 0.06 0.09 0.22 0.34 0.09 0.34 NA NA #> fixed 0.22 0.06 0.10 0.22 0.34 0.10 0.34 3026.5 1 #> random 0.22 0.08 0.07 0.22 0.37 0.07 0.37 6600.4 1"},{"path":"https://https://fbartos.github.io/RoBMA/articles/ReproducingBMA.html","id":"using-robma","dir":"Articles","previous_headings":"","what":"Using RoBMA","title":"Reproducing Bayesian Model-Averaged Meta-Analysis","text":"Now reproduce analysis RoBMA. set corresponding prior distributions effect sizes (μ\\mu) heterogeneity (τ\\tau), remove alternative prior distributions publication bias setting priors_bias = NULL. specify half-Cauchy prior distribution RoBMA::prior() function use regular Cauchy distribution truncate zero (note metaBMA RoBMA export prior() functions clash loading packages simultaneously). inverse-gamma prior distribution heterogeneity parameter default option (specify completeness). omit specifications null prior distributions effect size, heterogeneity (set spike 0 default), publication bias (set publication bias default). Note starting version 3.1, package includes NoBMA() function, allows users skip publication bias adjustment directly. Since metaBMA model-averages effect size estimates across models assuming presence effect, remove models assuming absence effect estimation ensemble priors_effect_null = NULL. Finally, set transformation = \"cohens_d\" estimate models Cohen’s d scale. RoBMA uses Fisher’s z scale default transforms estimated coefficients back scale used specifying prior distributions. speed computation setting parallel = TRUE, set seed reproducibility. output summary.RoBMA() function 2 parts. first one “Robust Bayesian Meta-Analysis” heading provides basic summary fitted models component types (presence Effect Heterogeneity). table summarizes prior posterior probabilities inclusion Bayes factors individual components. results half-Cauchy model specified testing show inclusion BF nearly identical one computed metaBMA package, BF10=33.11\\text{BF}_{10} = 33.11. second part ‘Model-averaged estimates’ heading displays parameter estimates. results unrestricted Cauchy model specified estimation show effect size estimate μ=0.22\\mu = 0.22, 95% CI [0.10, 0.35] also mirrors one obtained metaBMA package.","code":"library(RoBMA) fit_RoBMA_test <- RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study, priors_effect = prior( distribution = \"cauchy\", parameters = list(location = 0, scale = 1/sqrt(2)), truncation = list(0, Inf)), priors_heterogeneity = prior( distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = NULL, transformation = \"cohens_d\", seed = 1, parallel = TRUE) fit_RoBMA_est <- RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study, priors_effect = prior( distribution = \"cauchy\", parameters = list(location = 0, scale = 1/sqrt(2))), priors_heterogeneity = prior( distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = NULL, priors_effect_null = NULL, transformation = \"cohens_d\", seed = 2, parallel = TRUE) summary(fit_RoBMA_test) #> Call: #> RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study, #> transformation = \"cohens_d\", priors_effect = prior(distribution = \"cauchy\", #> parameters = list(location = 0, scale = 1/sqrt(2)), truncation = list(0, #> Inf)), priors_heterogeneity = prior(distribution = \"invgamma\", #> parameters = list(shape = 1, scale = 0.15)), priors_bias = NULL, #> parallel = TRUE, seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/4 0.500 0.971 33.112 #> Heterogeneity 2/4 0.500 0.214 0.273 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.213 0.217 0.000 0.348 #> tau 0.022 0.000 0.000 0.178 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). summary(fit_RoBMA_est) #> Call: #> RoBMA(d = power_pose$effectSize, se = power_pose$SE, study_names = power_pose$study, #> transformation = \"cohens_d\", priors_effect = prior(distribution = \"cauchy\", #> parameters = list(location = 0, scale = 1/sqrt(2))), #> priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, #> scale = 0.15)), priors_bias = NULL, priors_effect_null = NULL, #> parallel = TRUE, seed = 2) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 2/2 1.000 1.000 Inf #> Heterogeneity 1/2 0.500 0.200 0.250 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.220 0.220 0.096 0.346 #> tau 0.019 0.000 0.000 0.152 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."},{"path":"https://https://fbartos.github.io/RoBMA/articles/ReproducingBMA.html","id":"visualizing-the-results","dir":"Articles","previous_headings":"","what":"Visualizing the Results","title":"Reproducing Bayesian Model-Averaged Meta-Analysis","text":"RoBMA provides extensive options visualizing results. , visualize prior (grey) posterior (black) distribution mean parameter. visualize effect size model specified testing, notice things. function plots model-averaged estimates across models default, including models assuming absence effect. arrows represents probability spike, , value 0. secondary y-axis (right) shows probability value 0 decreased 0.50, 0.03 (also obtainable “Robust Bayesian Meta-Analysis” field summary.RoBMA() function). Furthermore, continuous prior distributions effect size alternative hypothesis truncated positive values, reflecting assumption effect size negative. can also visualize estimates individual models used ensemble. plot_models() function, visualizes effect size estimates 95% CI specified models estimation ensemble (Model 1 corresponds fixed effect model Model 2 random effect model). size square representing mean estimate reflects posterior model probability model, also displayed right-hand side panel. bottom part figure shows model-averaged estimate combination individual model posterior distributions weighted posterior model probabilities. last type visualization show forest plot. displays effect sizes original studies overall meta-analytic estimate single figure. can requested using forest() function. options provided plotting function, see documentation using ?plot.RoBMA(), ?plot_models(), ?forest().","code":"plot(fit_RoBMA_est, parameter = \"mu\", prior = TRUE, xlim = c(-1, 1)) plot(fit_RoBMA_test, parameter = \"mu\", prior = TRUE, xlim = c(-.5, 1)) plot_models(fit_RoBMA_est) forest(fit_RoBMA_est)"},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"set-up","dir":"Articles","previous_headings":"","what":"Set-up","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"start, need install JAGS (needed installation RoBMA package) R packages use analysis. Specifically RoBMA, weightr, metafor R packages. JAGS can downloaded JAGS website. Subsequently, install R packages install.packages() function. {r install.packages(c(\"RoBMA\", \"weightr\", \"metafor\")) happen use new M1 Mac machines Apple silicon, see blogpost outlining install JAGS M1. short, install Intel version R (Intel/x86-64) CRAN, Arm64 (Apple silicon) version. Note might changes installation process since blogpost written might JAGS version compatible Apple silicon available now. packages installed, can load workspace library() function.","code":"library(\"metafor\") library(\"weightr\") library(\"RoBMA\")"},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"lui-2015","dir":"Articles","previous_headings":"","what":"Lui (2015)","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"Lui (2015) studied acculturation mismatch () result contrast collectivist cultures Asian Latin immigrant groups individualist culture United States correlates intergenerational cultural conflict (ICC). Lui (2015) meta-analyzed 18 independent studies correlating ICC. standard reanalysis indicates significant effect increased ICC, r = 0.250, p < .001.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"data-manipulation","dir":"Articles","previous_headings":"Lui (2015)","what":"Data manipulation","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"First, load Lui2015.csv file R read.csv() function inspect first six data entries head() function (data set also included package can accessed via data(\"Lui2015\", package = \"RoBMA\") call). see data set contains three columns. first column called r contains effect sizes coded correlation coefficients, second column called n contains sample sizes, third column called study contains names individual studies. can access individual variables using data set name dollar ($) sign followed name column. example, can print effect sizes df$r command. printed output shows data set contains mostly positive effect sizes largest correlation coefficient r = 0.54.","code":"df <- read.csv(file = \"Lui2015.csv\") head(df) #> r n study #> 1 0.21 115 Ahn, Kim, & Park (2008) #> 2 0.29 283 Basanez et al. (2013) #> 3 0.22 80 Bounkeua (2007) #> 4 0.26 109 Hajizadeh (2009) #> 5 0.23 61 Hamid (2007) #> 6 0.54 107 Hwang & Wood (2009a) df$r #> [1] 0.21 0.29 0.22 0.26 0.23 0.54 0.56 0.29 0.26 0.02 -0.06 0.38 #> [13] 0.25 0.08 0.17 0.33 0.36 0.13"},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"effect-size-transformations","dir":"Articles","previous_headings":"Lui (2015)","what":"Effect size transformations","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"start analyzing data, transform effect sizes correlation coefficients ρ\\rho Fisher’s z. Correlation coefficients well suited meta-analysis (1) bounded range (-1, 1) non-linear increases near boundaries (2) standard error correlation coefficients related effect size. Fisher’s z transformation mitigates issues. unwinds (-1, 1) range (−∞-\\infty, ∞\\infty), makes sampling distribution approximately normal, breaks dependency standard errors effect sizes. apply transformation, use combine_data() function RoBMA package. pass correlation coefficients r argument, sample sizes n argument, set transformation argument \"fishers_z\" (study_names argument optional). function combine_data() saves transformed effect size estimates data frame called dfz, y column corresponds Fisher’s z transformation correlation coefficient se column corresponds standard error Fisher’s z. can also transform effect sizes according Cohen’s d transformation (utilize later fit selection models).","code":"dfz <- combine_data(r = df$r, n = df$n, study_names = df$study, transformation = \"fishers_z\") head(dfz) #> y se study_names study_ids weight #> 1 0.2131713 0.09449112 Ahn, Kim, & Park (2008) NA NA #> 2 0.2985663 0.05976143 Basanez et al. (2013) NA NA #> 3 0.2236561 0.11396058 Bounkeua (2007) NA NA #> 4 0.2661084 0.09712859 Hajizadeh (2009) NA NA #> 5 0.2341895 0.13130643 Hamid (2007) NA NA #> 6 0.6041556 0.09805807 Hwang & Wood (2009a) NA NA dfd <- combine_data(r = df$r, n = df$n, study_names = df$study, transformation = \"cohens_d\") head(dfd) #> y se study_names study_ids weight #> 1 0.4295790 0.1886397 Ahn, Kim, & Park (2008) NA NA #> 2 0.6060437 0.1215862 Basanez et al. (2013) NA NA #> 3 0.4510508 0.2264322 Bounkeua (2007) NA NA #> 4 0.5385205 0.1950065 Hajizadeh (2009) NA NA #> 5 0.4726720 0.2596249 Hamid (2007) NA NA #> 6 1.2831708 0.2123140 Hwang & Wood (2009a) NA NA"},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"re-analysis-with-random-effect-meta-analysis","dir":"Articles","previous_headings":"Lui (2015)","what":"Re-analysis with random effect meta-analysis","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"now estimate random effect meta-analysis rma() function imported metafor package (Wolfgang, 2010) verify arrive results reported Lui (2015) paper. yi argument used pass column name containing effect sizes, sei argument used pass column name containing standard errors, data argument used pass data frame containing variables. Indeed, find effect size estimate random effect meta-analysis corresponds one reported Lui (2015). important remember used Fisher’s z estimate models; therefore, estimated results Fisher’s z scale. transform effect size estimate correlation coefficients, can use z2r() function RoBMA package, Transforming effect size estimate results correlation coefficient ρ\\rho = 0.25.","code":"fit_rma <- rma(yi = y, sei = se, data = dfz) fit_rma #> #> Random-Effects Model (k = 18; tau^2 estimator: REML) #> #> tau^2 (estimated amount of total heterogeneity): 0.0229 (SE = 0.0107) #> tau (square root of estimated tau^2 value): 0.1513 #> I^2 (total heterogeneity / total variability): 77.79% #> H^2 (total variability / sampling variability): 4.50 #> #> Test for Heterogeneity: #> Q(df = 17) = 73.5786, p-val < .0001 #> #> Model Results: #> #> estimate se zval pval ci.lb ci.ub #> 0.2538 0.0419 6.0568 <.0001 0.1717 0.3359 *** #> #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 z2r(fit_rma$b) #> [,1] #> intrcpt 0.2484877"},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"pet-peese","dir":"Articles","previous_headings":"","what":"PET-PEESE","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"first publication bias adjustment perform PET-PEESE. PET-PEESE adjusts relationship effect sizes standard errors. knowledge, PET-PEESE currently implemented R-package. However, since PET PEESE weighted regressions effect sizes standard errors (PET) standard errors squared (PEESE), can estimate PET PEESE models lm() function. Inside lm() function call, specify y response variable (left hand side ~ sign) se predictor (right-hand side). Furthermore, specify weights argument allows us weight meta-regression inverse variance set data = dfz argument, specifies variables come transformed, dfz, data set. summary() function allows us explore details fitted model. (Intercept) coefficient refers meta-analytic effect size (corrected correlation standard errors). , important keep mind effect size estimate Fisher’s z scale. obtain estimate correlation scale z2r() function (pass estimated effect size using summary(fit_PET)$coefficients[\"(Intercept)\", \"Estimate\"] command, extracts estimate fitted model, equivalent simply pasting value directly z2r(-0.0008722083)). Since Fisher’s z transformation almost linear around zero, obtain almost identical estimate. importantly, since test effect size PET significant α=.10\\alpha = .10, interpret PET model. However, test effect size significant, fit interpret PEESE model. PEESE model can fitted analogous way, replacing predictor standard errors standard errors squared (need wrap se^2 predictor () tells R square predictor prior fitting model).","code":"fit_PET <- lm(y ~ se, weights = 1/se^2, data = dfz) summary(fit_PET) #> #> Call: #> lm(formula = y ~ se, data = dfz, weights = 1/se^2) #> #> Weighted Residuals: #> Min 1Q Median 3Q Max #> -3.8132 -0.9112 -0.0139 0.5166 3.3151 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) -0.0008722 0.1081247 -0.008 0.9937 #> se 2.8549650 1.3593450 2.100 0.0519 . #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 1.899 on 16 degrees of freedom #> Multiple R-squared: 0.2161, Adjusted R-squared: 0.1671 #> F-statistic: 4.411 on 1 and 16 DF, p-value: 0.05192 z2r(summary(fit_PET)$coefficients[\"(Intercept)\", \"Estimate\"]) #> [1] -0.000872208 fit_PEESE <- lm(y ~ I(se^2), weights = 1/se^2, data = dfz) summary(fit_PEESE) #> #> Call: #> lm(formula = y ~ I(se^2), data = dfz, weights = 1/se^2) #> #> Weighted Residuals: #> Min 1Q Median 3Q Max #> -3.7961 -0.9581 -0.1156 0.6718 3.4608 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 0.11498 0.06201 1.854 0.0822 . #> I(se^2) 15.58064 7.96723 1.956 0.0682 . #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 1.927 on 16 degrees of freedom #> Multiple R-squared: 0.1929, Adjusted R-squared: 0.1425 #> F-statistic: 3.824 on 1 and 16 DF, p-value: 0.06821"},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"selection-models","dir":"Articles","previous_headings":"","what":"Selection models","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"second publication bias adjustment perform selection models. Selection models adjust different publication probabilities different p-value intervals. Selection models implemented weightr package (weightfunct() function; Coburn et al. (2019)) newly also metafor package (selmodel() function; Wolfgang (2010)). First, use weightr implementation fit “4PSM” selection model specifies three distinct p-value intervals: (1) covering range significant p-values effect sizes expected direction (0.00-0.025), (2) covering range “marginally” significant p-values effect sizes expected direction (0.025-0.05), (3) covering range non-significant p-values (0.05-1). use Cohen’s d transformation correlation coefficients since better maintaining distribution test statistics. fit model, need pass effect sizes (dfd$y) effect argument variances (dfd$se^2) v argument (note need pass vector values directly since weightfunct() function allow us pass data frame directly previous functions). set steps = c(0.025, 0.05) specify appropriate cut-points (note steps correspond one-sided p-values), set table = TRUE obtain frequency p values specified intervals. Note warning message informing us fact data contain sufficient number p-values one p-value intervals. model output obtained printing fitted model object fit_4PSM shows one p-value (0.025, 0.05) interval. can deal issue joining “marginally” significant non-significant p-value interval, resulting “3PSM” model. new model suffer estimation problem due limited number p-values intervals, can now interpret results confidence. First, check test heterogeneity clearly rejects null hypothesis Q(df = 17) = 75.4999, $p$ = 5.188348e-09 (find evidence heterogeneity, proceeded fitting fixed effects version model specifying fe = TRUE argument). follow checking test publication bias likelihood ratio test comparing unadjusted adjusted estimate X^2(df = 1) = 3.107176, $p$ = 0.077948. result test slightly ambiguous – reject null hypothesis publication bias α=0.10\\alpha = 0.10 α=0.05\\alpha = 0.05. decide interpret estimated effect size, transform back correlation scale. However, time need use d2r() function since supplied effect sizes Cohen’s d (note effect size estimate corresponds second value fit_3PSM$adj_est object random effect model, alternatively, simply use d2r(0.3219641)). Alternatively, conducted analysis analogously metafor package. First, fit random effect meta-analysis Cohen’s d transformed effect sizes. Subsequently, used selmodel function, passing estimated random effect meta-analysis object specifying type = \"stepfun\" argument obtain step weight function setting appropriate steps steps = c(0.025) argument. output verifies results obtained previous analysis.","code":"fit_4PSM <- weightfunct(effect = dfd$y, v = dfd$se^2, steps = c(0.025, 0.05), table = TRUE) #> Warning in weightfunct(effect = dfd$y, v = dfd$se^2, steps = c(0.025, 0.05), : #> At least one of the p-value intervals contains three or fewer effect sizes, #> which may lead to estimation problems. Consider re-specifying the cutpoints. fit_4PSM #> #> Unadjusted Model (k = 18): #> #> tau^2 (estimated amount of total heterogeneity): 0.0920 (SE = 0.0423) #> tau (square root of estimated tau^2 value): 0.3034 #> #> Test for Heterogeneity: #> Q(df = 17) = 75.4999, p-val = 5.188348e-09 #> #> Model Results: #> #> estimate std.error z-stat p-val ci.lb ci.ub #> Intercept 0.516 0.08473 6.09 1.1283e-09 0.35 0.6821 #> #> Adjusted Model (k = 18): #> #> tau^2 (estimated amount of total heterogeneity): 0.1289 (SE = 0.0682) #> tau (square root of estimated tau^2 value): 0.3590 #> #> Test for Heterogeneity: #> Q(df = 17) = 75.4999, p-val = 5.188348e-09 #> #> Model Results: #> #> estimate std.error z-stat p-val ci.lb ci.ub #> Intercept 0.2675 0.2009 1.3311 0.18316 -0.1264 0.6613 #> 0.025 < p < 0.05 0.5008 0.5449 0.9191 0.35803 -0.5671 1.5688 #> 0.05 < p < 1 0.1535 0.1570 0.9777 0.32821 -0.1542 0.4611 #> #> Likelihood Ratio Test: #> X^2(df = 2) = 3.844252, p-val = 0.1463 #> #> Number of Effect Sizes per Interval: #> #> Frequency #> p-values <0.025 14 #> 0.025 < p-values < 0.05 1 #> 0.05 < p-values < 1 3 fit_3PSM <- weightfunct(effect = dfd$y, v = dfd$se^2, steps = c(0.025), table = TRUE) fit_3PSM #> #> Unadjusted Model (k = 18): #> #> tau^2 (estimated amount of total heterogeneity): 0.0920 (SE = 0.0423) #> tau (square root of estimated tau^2 value): 0.3034 #> #> Test for Heterogeneity: #> Q(df = 17) = 75.4999, p-val = 5.188348e-09 #> #> Model Results: #> #> estimate std.error z-stat p-val ci.lb ci.ub #> Intercept 0.516 0.08473 6.09 1.1283e-09 0.35 0.6821 #> #> Adjusted Model (k = 18): #> #> tau^2 (estimated amount of total heterogeneity): 0.1148 (SE = 0.0577) #> tau (square root of estimated tau^2 value): 0.3388 #> #> Test for Heterogeneity: #> Q(df = 17) = 75.4999, p-val = 5.188348e-09 #> #> Model Results: #> #> estimate std.error z-stat p-val ci.lb ci.ub #> Intercept 0.3220 0.1676 1.921 0.054698 -0.006484 0.6504 #> 0.025 < p < 1 0.2275 0.2004 1.135 0.256293 -0.165324 0.6204 #> #> Likelihood Ratio Test: #> X^2(df = 1) = 3.107176, p-val = 0.077948 #> #> Number of Effect Sizes per Interval: #> #> Frequency #> p-values <0.025 14 #> 0.025 < p-values < 1 4 d2r(fit_3PSM$adj_est[2]) #> [1] 0.1589358 fit_rma_d <- rma(yi = y, sei = se, data = dfd) fit_sel_d <- selmodel(fit_rma_d, type = \"stepfun\", steps = c(0.025)) fit_sel_d #> #> Random-Effects Model (k = 18; tau^2 estimator: ML) #> #> tau^2 (estimated amount of total heterogeneity): 0.1148 (SE = 0.0577) #> tau (square root of estimated tau^2 value): 0.3388 #> #> Test for Heterogeneity: #> LRT(df = 1) = 32.7499, p-val < .0001 #> #> Model Results: #> #> estimate se zval pval ci.lb ci.ub #> 0.3220 0.1676 1.9214 0.0547 -0.0065 0.6504 . #> #> Test for Selection Model Parameters: #> LRT(df = 1) = 3.1072, p-val = 0.0779 #> #> Selection Model Results: #> #> k estimate se zval pval ci.lb ci.ub #> 0 < p <= 0.025 14 1.0000 --- --- --- --- --- #> 0.025 < p <= 1 4 0.2275 0.2004 -3.8537 0.0001 0.0000 0.6204 *** #> #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"robust-bayesian-meta-analysis","dir":"Articles","previous_headings":"","what":"Robust Bayesian meta-analysis","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"third final publication bias adjustment perform robust Bayesian meta-analysis (RoBMA). RoBMA uses Bayesian model-averaging combine inference PET-PEESE selection models. use RoBMA R package (RoBMA() function; Bartoš & Maier (2020)) fit default 36 model ensemble (called RoBMA-PSMA) based orthogonal combination models assuming presence absence effect size, heterogeneity, publication bias. models assuming presence publication bias split six weight function models models utilizing PET PEESE publication bias adjustment. fit model, can directly pass original correlation coefficients r argument sample sizes n argument – RoBMA() function internally transform Fisher’s z scale , default, return estimates Cohen’s d scale used specify prior distributions (settings can changed prior_scale transformation arguments, output can conveniently transformed later). set model argument \"PSMA\" fit 36 model ensemble use seed argument make analysis reproducible (uses MCMC sampling contrast previous methods). turn parallel estimation setting parallel = TRUE argument (parallel processing might cases fail, try rerunning model one time turning parallel processing case). step can take time depending CPU. example, take approximately 1 minute fast CPU (e.g., AMD Ryzen 3900x 12c/24t) ten minutes longer slower CPUs (e.g., 2.7 GHz Intel Core i5). use summary() function explore details fitted model. printed output consists two parts. first table called Components summary contains information fitted models. tells us estimated ensemble 18/36 models assuming presence effect, 18/36 models assuming presence heterogeneity, 32/36 models assuming presence publication bias. second column summarizes prior model probabilities models assuming either presence individual components – , see presence absence components balanced priori. third column contains information posterior probability models assuming presence components – can observe posterior model probabilities models assuming presence effect slightly increased 0.552. last column contains information evidence favor presence components. Evidence presence effect undecided; models assuming presence effect 1.232 times likely given data models assuming absence effect. However, find overwhelming evidence favor heterogeneity, models assuming presence heterogeneity 19,168 times likely given data models assuming absence heterogeneity, moderate evidence favor publication bias. name indicates, second table called Model-averaged estimates contains information model-averaged estimates. first row labeled mu corresponds model-averaged effect size estimate (Cohen’s d scale) second row labeled tau corresponds model-averaged heterogeneity estimates. estimated model-averaged weights different p-value intervals PET PEESE regression coefficients. convert estimates correlation coefficients adding output_scale = \"r\" argument summary function. Now, obtained model-averaged effect size estimate correlation scale. interested estimates model-averaging across models assuming presence effect (effect size estimate), heterogeneity (heterogeneity estimate), publication bias (publication bias weights PET PEESE regression coefficients), added conditional = TRUE argument summary function. quick textual summary model can also generated interpret() function. can also obtain summary information individual models specifying type = \"models\" option. resulting table shows prior posterior model probabilities inclusion Bayes factors individual models (also set short_name = TRUE argument reducing width output abbreviating names prior distributions). obtain summary individual model diagnostics, set type = \"diagnostics\" argument. resulting table provides information maximum MCMC error, relative MCMC error, minimum ESS, maximum R-hat aggregating parameters model. can see, obtain acceptable ESS R-hat diagnostic values. Finally, can also plot model-averaged posterior distribution plot() function. set prior = TRUE argument include prior distribution grey line (arrow point density zero) output_scale = \"r\" transform posterior distribution correlation scale (default figure output Cohen’s d scale). (par(mar = c(4, 4, 1, 4)) call increases left margin figure, secondary y-axis text cut .)","code":"fit_RoBMA <- RoBMA(r = df$r, n = df$n, seed = 1, model = \"PSMA\", parallel = TRUE) summary(fit_RoBMA) #> Call: #> RoBMA(r = df$r, n = df$n, model_type = \"PSMA\", parallel = TRUE, #> save = \"min\", seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 18/36 0.500 0.552 1.232 #> Heterogeneity 18/36 0.500 1.000 19168.311 #> Bias 32/36 0.500 0.845 5.436 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.195 0.087 -0.008 0.598 #> tau 0.330 0.307 0.166 0.597 #> omega[0,0.025] 1.000 1.000 1.000 1.000 #> omega[0.025,0.05] 0.936 1.000 0.438 1.000 #> omega[0.05,0.5] 0.740 1.000 0.065 1.000 #> omega[0.5,0.95] 0.697 1.000 0.028 1.000 #> omega[0.95,0.975] 0.704 1.000 0.028 1.000 #> omega[0.975,1] 0.713 1.000 0.028 1.000 #> PET 0.828 0.000 0.000 3.291 #> PEESE 0.802 0.000 0.000 10.805 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). #> (Estimated publication weights omega correspond to one-sided p-values.) summary(fit_RoBMA, output_scale = \"r\") #> Call: #> RoBMA(r = df$r, n = df$n, model_type = \"PSMA\", parallel = TRUE, #> save = \"min\", seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 18/36 0.500 0.552 1.232 #> Heterogeneity 18/36 0.500 1.000 19168.311 #> Bias 32/36 0.500 0.845 5.436 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.095 0.043 -0.004 0.286 #> tau 0.165 0.154 0.083 0.299 #> omega[0,0.025] 1.000 1.000 1.000 1.000 #> omega[0.025,0.05] 0.936 1.000 0.438 1.000 #> omega[0.05,0.5] 0.740 1.000 0.065 1.000 #> omega[0.5,0.95] 0.697 1.000 0.028 1.000 #> omega[0.95,0.975] 0.704 1.000 0.028 1.000 #> omega[0.975,1] 0.713 1.000 0.028 1.000 #> PET 0.828 0.000 0.000 3.291 #> PEESE 1.603 0.000 0.000 21.610 #> The effect size estimates are summarized on the correlation scale and heterogeneity is summarized on the Fisher's z scale (priors were specified on the Cohen's d scale). #> (Estimated publication weights omega correspond to one-sided p-values.) interpret(fit_RoBMA, output_scale = \"r\") #> [1] \"Robust Bayesian meta-analysis found weak evidence in favor of the effect, BF_10 = 1.23, with mean model-averaged estimate correlation = 0.095, 95% CI [-0.004, 0.286]. Robust Bayesian meta-analysis found strong evidence in favor of the heterogeneity, BF^rf = 19168.31, with mean model-averaged estimate tau = 0.165, 95% CI [0.083, 0.299]. Robust Bayesian meta-analysis found moderate evidence in favor of the publication bias, BF_pb = 5.44.\" summary(fit_RoBMA, type = \"models\", short_name = TRUE) #> Call: #> RoBMA(r = df$r, n = df$n, model_type = \"PSMA\", parallel = TRUE, #> save = \"min\", seed = 1) #> #> Robust Bayesian meta-analysis #> Models overview: #> Model Prior Effect Prior Heterogeneity #> 1 S(0) S(0) #> 2 S(0) S(0) #> 3 S(0) S(0) #> 4 S(0) S(0) #> 5 S(0) S(0) #> 6 S(0) S(0) #> 7 S(0) S(0) #> 8 S(0) S(0) #> 9 S(0) S(0) #> 10 S(0) Ig(1, 0.15) #> 11 S(0) Ig(1, 0.15) #> 12 S(0) Ig(1, 0.15) #> 13 S(0) Ig(1, 0.15) #> 14 S(0) Ig(1, 0.15) #> 15 S(0) Ig(1, 0.15) #> 16 S(0) Ig(1, 0.15) #> 17 S(0) Ig(1, 0.15) #> 18 S(0) Ig(1, 0.15) #> 19 N(0, 1) S(0) #> 20 N(0, 1) S(0) #> 21 N(0, 1) S(0) #> 22 N(0, 1) S(0) #> 23 N(0, 1) S(0) #> 24 N(0, 1) S(0) #> 25 N(0, 1) S(0) #> 26 N(0, 1) S(0) #> 27 N(0, 1) S(0) #> 28 N(0, 1) Ig(1, 0.15) #> 29 N(0, 1) Ig(1, 0.15) #> 30 N(0, 1) Ig(1, 0.15) #> 31 N(0, 1) Ig(1, 0.15) #> 32 N(0, 1) Ig(1, 0.15) #> 33 N(0, 1) Ig(1, 0.15) #> 34 N(0, 1) Ig(1, 0.15) #> 35 N(0, 1) Ig(1, 0.15) #> 36 N(0, 1) Ig(1, 0.15) #> Prior Bias Prior prob. log(marglik) #> 0.125 -74.67 #> omega[2s: .05] ~ CumD(1, 1) 0.010 -49.60 #> omega[2s: .1, .05] ~ CumD(1, 1, 1) 0.010 -47.53 #> omega[1s: .05] ~ CumD(1, 1) 0.010 -41.70 #> omega[1s: .05, .025] ~ CumD(1, 1, 1) 0.010 -38.03 #> omega[1s: .5, .05] ~ CumD(1, 1, 1) 0.010 -44.41 #> omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1) 0.010 -40.79 #> PET ~ C(0, 1)[0, Inf] 0.031 -5.01 #> PEESE ~ C(0, 5)[0, Inf] 0.031 -12.17 #> 0.125 -6.95 #> omega[2s: .05] ~ CumD(1, 1) 0.010 -5.96 #> omega[2s: .1, .05] ~ CumD(1, 1, 1) 0.010 -5.09 #> omega[1s: .05] ~ CumD(1, 1) 0.010 2.72 #> omega[1s: .05, .025] ~ CumD(1, 1, 1) 0.010 2.93 #> omega[1s: .5, .05] ~ CumD(1, 1, 1) 0.010 2.91 #> omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1) 0.010 3.30 #> PET ~ C(0, 1)[0, Inf] 0.031 3.62 #> PEESE ~ C(0, 5)[0, Inf] 0.031 1.62 #> 0.125 -13.17 #> omega[2s: .05] ~ CumD(1, 1) 0.010 -13.10 #> omega[2s: .1, .05] ~ CumD(1, 1, 1) 0.010 -12.87 #> omega[1s: .05] ~ CumD(1, 1) 0.010 -12.75 #> omega[1s: .05, .025] ~ CumD(1, 1, 1) 0.010 -12.86 #> omega[1s: .5, .05] ~ CumD(1, 1, 1) 0.010 -13.29 #> omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1) 0.010 -13.25 #> PET ~ C(0, 1)[0, Inf] 0.031 -7.07 #> PEESE ~ C(0, 5)[0, Inf] 0.031 -7.58 #> 0.125 1.79 #> omega[2s: .05] ~ CumD(1, 1) 0.010 1.75 #> omega[2s: .1, .05] ~ CumD(1, 1, 1) 0.010 2.16 #> omega[1s: .05] ~ CumD(1, 1) 0.010 3.11 #> omega[1s: .05, .025] ~ CumD(1, 1, 1) 0.010 3.01 #> omega[1s: .5, .05] ~ CumD(1, 1, 1) 0.010 2.98 #> omega[1s: .5, .05, .025] ~ CumD(1, 1, 1, 1) 0.010 3.06 #> PET ~ C(0, 1)[0, Inf] 0.031 2.75 #> PEESE ~ C(0, 5)[0, Inf] 0.031 2.55 #> Post. prob. Inclusion BF #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.001 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.001 #> 0.000 0.001 #> 0.033 3.231 #> 0.041 4.025 #> 0.040 3.919 #> 0.059 5.927 #> 0.243 9.957 #> 0.033 1.055 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.000 0.000 #> 0.155 1.287 #> 0.012 1.201 #> 0.019 1.822 #> 0.048 4.831 #> 0.044 4.347 #> 0.043 4.223 #> 0.046 4.617 #> 0.102 3.504 #> 0.083 2.797 summary(fit_RoBMA, type = \"diagnostics\") #> Call: #> RoBMA(r = df$r, n = df$n, model_type = \"PSMA\", parallel = TRUE, #> save = \"min\", seed = 1) #> #> Robust Bayesian meta-analysis #> Diagnostics overview: #> Model Prior Effect Prior Heterogeneity #> 1 Spike(0) Spike(0) #> 2 Spike(0) Spike(0) #> 3 Spike(0) Spike(0) #> 4 Spike(0) Spike(0) #> 5 Spike(0) Spike(0) #> 6 Spike(0) Spike(0) #> 7 Spike(0) Spike(0) #> 8 Spike(0) Spike(0) #> 9 Spike(0) Spike(0) #> 10 Spike(0) InvGamma(1, 0.15) #> 11 Spike(0) InvGamma(1, 0.15) #> 12 Spike(0) InvGamma(1, 0.15) #> 13 Spike(0) InvGamma(1, 0.15) #> 14 Spike(0) InvGamma(1, 0.15) #> 15 Spike(0) InvGamma(1, 0.15) #> 16 Spike(0) InvGamma(1, 0.15) #> 17 Spike(0) InvGamma(1, 0.15) #> 18 Spike(0) InvGamma(1, 0.15) #> 19 Normal(0, 1) Spike(0) #> 20 Normal(0, 1) Spike(0) #> 21 Normal(0, 1) Spike(0) #> 22 Normal(0, 1) Spike(0) #> 23 Normal(0, 1) Spike(0) #> 24 Normal(0, 1) Spike(0) #> 25 Normal(0, 1) Spike(0) #> 26 Normal(0, 1) Spike(0) #> 27 Normal(0, 1) Spike(0) #> 28 Normal(0, 1) InvGamma(1, 0.15) #> 29 Normal(0, 1) InvGamma(1, 0.15) #> 30 Normal(0, 1) InvGamma(1, 0.15) #> 31 Normal(0, 1) InvGamma(1, 0.15) #> 32 Normal(0, 1) InvGamma(1, 0.15) #> 33 Normal(0, 1) InvGamma(1, 0.15) #> 34 Normal(0, 1) InvGamma(1, 0.15) #> 35 Normal(0, 1) InvGamma(1, 0.15) #> 36 Normal(0, 1) InvGamma(1, 0.15) #> Prior Bias max[error(MCMC)] #> NA #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.00024 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.00295 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.00014 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.00326 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.00033 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.00309 #> PET ~ Cauchy(0, 1)[0, Inf] 0.00236 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.01223 #> 0.00118 #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.00296 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.00295 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.00110 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.00331 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.00357 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.00307 #> PET ~ Cauchy(0, 1)[0, Inf] 0.00454 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.02470 #> 0.00038 #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.00303 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.00290 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.00309 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.00278 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.00332 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.00293 #> PET ~ Cauchy(0, 1)[0, Inf] 0.03247 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.05228 #> 0.00090 #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.00308 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.00293 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.00477 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.00340 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.00543 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.00499 #> PET ~ Cauchy(0, 1)[0, Inf] 0.04070 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.07238 #> max[error(MCMC)/SD] min(ESS) max(R-hat) #> NA NA NA #> 0.016 4158 1.000 #> 0.016 3793 1.000 #> 0.015 4622 1.000 #> 0.017 3357 1.000 #> 0.017 3509 1.001 #> 0.018 3064 1.001 #> 0.010 9917 1.001 #> 0.010 9589 1.000 #> 0.010 9632 1.001 #> 0.013 5518 1.002 #> 0.015 4565 1.001 #> 0.015 4395 1.001 #> 0.015 4502 1.002 #> 0.018 3206 1.001 #> 0.017 3480 1.001 #> 0.012 7342 1.001 #> 0.012 7051 1.000 #> 0.010 9712 1.001 #> 0.013 5522 1.000 #> 0.015 4382 1.001 #> 0.013 5771 1.000 #> 0.014 4859 1.001 #> 0.015 4430 1.000 #> 0.016 4135 1.001 #> 0.042 565 1.005 #> 0.024 1678 1.001 #> 0.011 7736 1.000 #> 0.014 5254 1.001 #> 0.016 4103 1.001 #> 0.021 2240 1.001 #> 0.020 2527 1.001 #> 0.026 1529 1.007 #> 0.024 1756 1.000 #> 0.038 692 1.001 #> 0.024 1765 1.005 par(mar = c(4, 4, 1, 4)) plot(fit_RoBMA, prior = TRUE, output_scale = \"r\", )"},{"path":"https://https://fbartos.github.io/RoBMA/articles/Tutorial.html","id":"specifying-different-priors","dir":"Articles","previous_headings":"Robust Bayesian meta-analysis","what":"Specifying Different Priors","title":"Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis","text":"RoBMA package allows us fit ensembles highly customized meta-analytic models. reproduce ensemble perinull directional hypothesis test Appendix (see R package vignettes examples details). Instead using fully pre-specified model model = \"PSMA\" argument, explicitly specify prior distribution models assuming presence effect priors_effect = prior(\"normal\", parameters = list(mean = 0.60, sd = 0.20), truncation = list(0, Inf)) argument, assigns Normal(0.60, 0.20) distribution bounded positive numbers μ\\mu parameter (note prior distribution specified Cohen’s d scale, corresponding 95% prior probability mass contained approximately ρ\\rho = (0.10, 0.45) interval). Similarly, also replace default prior distribution models assuming absence effect perinull hypothesis priors_effect_null = prior(\"normal\", parameters = list(mean = 0, sd = 0.10)) argument sets 95% prior probability mass values ρ\\rho = (-0.10, 0.10) interval. previously, can use summary() function inspect model fit verify specified models correspond settings.","code":"fit_RoBMA2 <- RoBMA(r = df$r, n = df$n, seed = 2, parallel = TRUE, priors_effect = prior(\"normal\", parameters = list(mean = 0.60, sd = 0.20), truncation = list(0, Inf)), priors_effect_null = prior(\"normal\", parameters = list(mean = 0, sd = 0.10))) summary(fit_RoBMA2, type = \"models\") #> Call: #> RoBMA(r = df$r, n = df$n, priors_effect = prior(\"normal\", parameters = list(mean = 0.6, #> sd = 0.2), truncation = list(0, Inf)), priors_effect_null = prior(\"normal\", #> parameters = list(mean = 0, sd = 0.1)), parallel = TRUE, #> save = \"min\", seed = 2) #> #> Robust Bayesian meta-analysis #> Models overview: #> Model Prior Effect Prior Heterogeneity #> 1 Normal(0, 0.1) Spike(0) #> 2 Normal(0, 0.1) Spike(0) #> 3 Normal(0, 0.1) Spike(0) #> 4 Normal(0, 0.1) Spike(0) #> 5 Normal(0, 0.1) Spike(0) #> 6 Normal(0, 0.1) Spike(0) #> 7 Normal(0, 0.1) Spike(0) #> 8 Normal(0, 0.1) Spike(0) #> 9 Normal(0, 0.1) Spike(0) #> 10 Normal(0, 0.1) InvGamma(1, 0.15) #> 11 Normal(0, 0.1) InvGamma(1, 0.15) #> 12 Normal(0, 0.1) InvGamma(1, 0.15) #> 13 Normal(0, 0.1) InvGamma(1, 0.15) #> 14 Normal(0, 0.1) InvGamma(1, 0.15) #> 15 Normal(0, 0.1) InvGamma(1, 0.15) #> 16 Normal(0, 0.1) InvGamma(1, 0.15) #> 17 Normal(0, 0.1) InvGamma(1, 0.15) #> 18 Normal(0, 0.1) InvGamma(1, 0.15) #> 19 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 20 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 21 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 22 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 23 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 24 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 25 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 26 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 27 Normal(0.6, 0.2)[0, Inf] Spike(0) #> 28 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 29 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 30 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 31 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 32 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 33 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 34 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 35 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> 36 Normal(0.6, 0.2)[0, Inf] InvGamma(1, 0.15) #> Prior Bias Prior prob. #> 0.125 #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010 #> PET ~ Cauchy(0, 1)[0, Inf] 0.031 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031 #> 0.125 #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010 #> PET ~ Cauchy(0, 1)[0, Inf] 0.031 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031 #> 0.125 #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010 #> PET ~ Cauchy(0, 1)[0, Inf] 0.031 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031 #> 0.125 #> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010 #> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010 #> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010 #> PET ~ Cauchy(0, 1)[0, Inf] 0.031 #> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031 #> log(marglik) Post. prob. Inclusion BF #> -18.84 0.000 0.000 #> -17.66 0.000 0.000 #> -17.06 0.000 0.000 #> -17.35 0.000 0.000 #> -17.04 0.000 0.000 #> -18.11 0.000 0.000 #> -17.69 0.000 0.000 #> -5.24 0.000 0.000 #> -7.61 0.000 0.000 #> -3.20 0.000 0.003 #> -1.45 0.000 0.022 #> -0.42 0.001 0.061 #> 3.01 0.020 1.939 #> 3.19 0.024 2.317 #> 3.09 0.022 2.104 #> 3.46 0.031 3.062 #> 3.64 0.112 3.909 #> 2.35 0.031 0.986 #> -11.84 0.000 0.000 #> -11.88 0.000 0.000 #> -11.71 0.000 0.000 #> -11.54 0.000 0.000 #> -11.70 0.000 0.000 #> -12.05 0.000 0.000 #> -12.07 0.000 0.000 #> -8.38 0.000 0.000 #> -7.36 0.000 0.000 #> 3.35 0.337 3.564 #> 3.13 0.023 2.190 #> 3.42 0.030 2.951 #> 4.12 0.061 6.123 #> 3.85 0.046 4.602 #> 3.94 0.050 5.027 #> 3.84 0.046 4.572 #> 3.23 0.074 2.492 #> 3.44 0.092 3.132"},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"František Bartoš. Author, maintainer. Maximilian Maier. Author. Eric-Jan Wagenmakers. Thesis advisor. Joris Goosen. Contributor. Matthew Denwood. Copyright holder. Original copyright holder modified code indicated. Martyn Plummer. Copyright holder. Original copyright holder modified code indicated.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Bartoš F, Maier M (2020). “RoBMA: R Package Robust Bayesian Meta-Analyses.” R package version 3.2.0, https://CRAN.R-project.org/package=RoBMA.","code":"@Misc{, title = {RoBMA: An R Package for Robust Bayesian Meta-Analyses}, author = {František Bartoš and Maximilian Maier}, year = {2020}, note = {R package version 3.2.0}, url = {https://CRAN.R-project.org/package=RoBMA}, }"},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/index.html","id":"robust-bayesian-meta-analysis-robma","dir":"","previous_headings":"","what":"Robust Bayesian Meta-Analysis (RoBMA)","title":"Robust Bayesian Meta-Analyses","text":"package estimates ensemble meta-analytic models (assuming either presence absence effect, heterogeneity, publication bias) uses Bayesian model averaging combine . ensemble uses Bayes factors test presence absence individual components (e.g., effect vs. effect) model-averages parameter estimates based posterior model probabilities. user can define wide range prior distributions effect size, heterogeneity, publication bias components (including selection, PET, PEESE style models). package provides convenient functions summary, visualizations, fit diagnostics. See manuscripts technical details examples: Bartoš, Maier, Stanley, et al. (2023) (https://doi.org/10.31234/osf.io/98xb5) extends RoBMA-PSMA meta-regression Bartoš, Otte, et al. (2023) (https://doi.org/10.48550/arXiv.2306.11468) outlines binomial-normal Bayesian model-averaged meta-analysis binary outcomes (+ develops informed prior distributions log , log RR, RD, log HR medical settings, also see Bartoš et al. (2021) informed prior distributions Cohen’s d, based Cochrane Database Systematic Reviews) Bartoš, Maier, Wagenmakers, et al. (2023) (https://doi.org/10.1002/jrsm.1594) describes newest version publication bias adjustment, RoBMA-PSMA, combines selection models PET-PEESE, Maier et al. (2023) (https://doi.org/10.1037/met0000405) introduces RoBMA framework original version method, Bartoš et al. (2022) (https://doi.org/10.1177/25152459221109259) provides accessible tutorial method including implementation user-friendly graphical user interface JASP (JASP Team, 2020) also prepared multiple vignettes illustrate functionality package: Tutorial: Adjusting publication bias JASP R - Selection models, PET-PEESE, Robust Bayesian meta-analysis Reproducing Bayesian model-averaged meta-analysis (BMA) Robust Bayesian model-averaged meta-regression Hierarchical Bayesian model-averaged meta-analysis Informed Bayesian model-averaged meta-analysis medicine Informed Bayesian model-averaged meta-analysis binary outcomes Fitting custom meta-analytic ensembles","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/index.html","id":"backwards-compatibility","dir":"","previous_headings":"Updates","what":"Backwards Compatibility","title":"Robust Bayesian Meta-Analyses","text":"Please note major releases RoBMA break backwards compatibility. latest version RoBMA 1 can installed using latest version RoBMA 2 can installed using (use source packages archived OSF repositories associated corresponding projects.)","code":"remotes::install_version(\"RoBMA\", version = \"1.2.1\") remotes::install_version(\"RoBMA\", version = \"2.3.2\")"},{"path":"https://https://fbartos.github.io/RoBMA/index.html","id":"news","dir":"","previous_headings":"Updates","what":"News","title":"Robust Bayesian Meta-Analyses","text":"3.0 version brings several features package: meta-regression models via RoBMA.reg() function binomial-normal meta-analytic models via BiBMA() function publication bias unadjusted models via NoBMA() NoBMA.reg() functions (wrappers around RoBMA() RoBMA.reg()) marginal summaries plots regression models via marginal_summary() marginal_plot() function prediction intervals, ^2, H^2 statistics using summary_heterogeneity() function 2.0 version brought several updates package: naming arguments specifying prior distributions different parameters/components models changed (priors_mu -> priors_effect, priors_tau -> priors_heterogeneity, priors_omega -> priors_bias), prior distributions specifying weight functions now use dedicated function (prior(distribution = \"two.sided\", parameters = ...) -> prior_weightfunction(distribution = \"two.sided\", parameters = ...)), new dedicated function specifying publication bias adjustment component / heterogeneity component (prior_none()), new dedicated functions specifying models PET PEESE publication bias adjustments (prior_PET(distribution = \"Cauchy\", parameters = ...) prior_PEESE(distribution = \"Cauchy\", parameters = ...)), new default prior distribution specification publication bias adjustment part models (corresponding RoBMA-PSMA model Bartoš, Maier, Wagenmakers, et al. (2023)), new model_type argument allowing specify different “pre-canned” models (\"PSMA\" = RoBMA-PSMA, \"PP\" = RoBMA-PP, \"2w\" = corresponding Maier et al. (2023)), combine_data function allows combination different effect sizes / variability measures common effect size measure (also used within RoBMA function) better improved automatic fitting procedure now enabled default (can turned autofit = FALSE) prior distributions can specified different scale supplied effect sizes (package fits model Fisher’s z scale back transforms results back scale used prior distributions specification, Cohen’s d default, can overwritten prior_scale transformation arguments), new prior distributions, e.g., beta fixed weight functions, plenty small changes arguments, output, etc…","code":""},{"path":"https://https://fbartos.github.io/RoBMA/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Robust Bayesian Meta-Analyses","text":"package requires JAGS 4.3.2 installed. release version can installed CRAN: development version package can installed GitHub:","code":"install.packages(\"RoBMA\") devtools::install_github(\"FBartos/RoBMA\")"},{"path":"https://https://fbartos.github.io/RoBMA/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Robust Bayesian Meta-Analyses","text":"illustrate functionality package, fit RoBMA-PSMA model example Bartoš, Maier, Wagenmakers, et al. (2023) adjust publication bias infamous Bem (2011) “Feeling future” pre-cognition study. RoBMA-PSMA model combines six selection models PET-PEESE adjust publication bias. pre-print, analyze data described Bem et al. (2011) reply methodological critiques. First, load package data set included package. , fit meta-analytic model ensemble composed 36 models (new default settings RoBMA fitting function). models represent possible combinations prior distributions following components: spike zero, representing null hypothesis absence effect standard normal distribution, representing alternative hypothesis presence effect spike zero, representing null hypothesis absence heterogeneity (.e., fixed effect meta-analysis) inverse gamma distribution shape = 1 scale = 0.15, based Erp et al. (2017), representing alternative hypothesis presence heterogeneity (.e., random effect meta-analysis) prior distribution, representing absence publication bias eight prior distributions specifying two two-sided weight functions, four one-sided weight functions, PET PEESE publication bias adjustment, representing presence publication bias prior odds components default set make three model categories equally likely priory (0.5 prior probability presence effect, 0.5 prior probability presence heterogeneity, 0.5 prior probability presence publication bias). prior model probability publication bias adjustment component split equally among selection models represented six weightfunctions PET-PEESE models. main summary can obtained using summary.RoBMA() function. first table shows overview ensemble composition. number models, prior posterior model probabilities, inclusion Bayes factor ensemble components representing alternative hypothesis presence effect, heterogeneity, publication bias, can see data show weak evidence, barely worth mentioning, presence effect (BF10=0.479\\text{BF}_{10} = 0.479 -> BF01=2.09\\text{BF}_{01} = 2.09), moderate evidence absence heterogeneity (BFrf=0.143\\text{BF}_{\\text{rf}} = 0.143 -> BFfr=7.00BF_{\\text{fr}} = 7.00), strong evidence presence publication bias (BFpb=16.32\\text{BF}_{\\text{pb}} = 16.32). second table shows model-averaged estimates weighted individual models’ posterior probabilities. mean estimate μ=0.037\\mu =0.037, 95% CI [-0.041, 0.213], close zero, corresponding priory expected absence pre-cognition. heterogeneity estimate τ\\tau probability mass around zero due higher support models assuming absence heterogeneity. parameters omega, representing publication weights p-value interval decreasing increasing p-values, showing publication bias, well non zero PET PEESE estimates. can visualize estimated mean heterogeneity parameters using plot.RoBMA() function. arrows figures represent point probability mass μ=0\\mu = 0 τ=0\\tau = 0, corresponding null hypotheses absence effect heterogeneity, increasing posterior model probability 0.5 0.676 0.875 respectively. can visualize publication bias adjustments selection models, visualizing posterior estimate model-averaged weightfunction shows sharp decrease publication weights studies p-values “marginal significance” (0.10) level, PET-PEESE publication bias adjustment, visualizing individual studies’ standard errors effect sizes diamonds model-averaged estimate regression lines shows steady increase effect sizes increasing standard errors. usual meta-analytic forest plot can obtained forest() function, visualization effect size estimates models assuming presence effect can obtained plot_models() function. Apart plotting, individual model performance can inspected using summary.RoBMA() function argument type = \"models\" overview individual model MCMC diagnostics can obtained setting type = \"diagnostics\" (shown lack space). can also visualize MCMC diagnostics using diagnostics function. function can display chains type = \"chain\" / posterior sample densities type = \"densities\", averaged auto-correlations type = \"autocorrelation\". , request chains trace plot μ\\mu parameter complex model setting show_models = 36 (model numbers can obtained summary function type = \"models\" argument). package allows fit highly customized models different prior distribution functions, prior model probabilities, provides visualization options. See documentation find specific functions: RoBMA(), priors(), plot.RoBMA(). main package functionality also implemented within Meta Analysis module JASP 0.14 (JASP Team, 2020) soon updated accommodate 2.0 version package.","code":"library(RoBMA) #> Loading required namespace: runjags #> Loading required namespace: mvtnorm data(\"Bem2011\", package = \"RoBMA\") Bem2011 #> d se study #> 1 0.25 0.10155048 Detection of Erotic Stimuli #> 2 0.20 0.08246211 Avoidance of Negative Stimuli #> 3 0.26 0.10323629 Retroactive Priming I #> 4 0.23 0.10182427 Retroactive Priming II #> 5 0.22 0.10120277 Retroactive Habituation I - Negative trials #> 6 0.15 0.08210765 Retroactive Habituation II - Negative trials #> 7 0.09 0.07085372 Retroactive Induction of Boredom #> 8 0.19 0.10089846 Facilitation of Recall I #> 9 0.42 0.14752627 Facilitation of Recall II fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, seed = 1) summary(fit) #> Call: #> RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, #> seed = 1) #> #> Robust Bayesian meta-analysis #> Components summary: #> Models Prior prob. Post. prob. Inclusion BF #> Effect 18/36 0.500 0.324 0.479 #> Heterogeneity 18/36 0.500 0.125 0.143 #> Bias 32/36 0.500 0.942 16.323 #> #> Model-averaged estimates: #> Mean Median 0.025 0.975 #> mu 0.037 0.000 -0.041 0.213 #> tau 0.010 0.000 0.000 0.113 #> omega[0,0.025] 1.000 1.000 1.000 1.000 #> omega[0.025,0.05] 0.935 1.000 0.338 1.000 #> omega[0.05,0.5] 0.780 1.000 0.009 1.000 #> omega[0.5,0.95] 0.768 1.000 0.007 1.000 #> omega[0.95,0.975] 0.786 1.000 0.007 1.000 #> omega[0.975,1] 0.801 1.000 0.007 1.000 #> PET 0.759 0.000 0.000 2.805 #> PEESE 6.183 0.000 0.000 25.463 #> The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale). #> (Estimated publication weights omega correspond to one-sided p-values.) plot(fit, parameter = \"mu\", xlim = c(-0.5, 0.5)) plot(fit, parameter = \"tau\") plot(fit, parameter = \"weightfunction\", rescale_x = TRUE) plot(fit, parameter = \"PET-PEESE\", xlim = c(0, 0.25)) forest(fit) plot_models(fit, conditional = TRUE) diagnostics(fit, parameter = \"mu\", type = \"chains\", show_models = 36)"},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/Anderson2010.html","id":null,"dir":"Reference","previous_headings":"","what":"27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria — Anderson2010","title":"27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria — Anderson2010","text":"data set contains correlation coefficients, sample sizes, labels 27 experimental studies focusing effect violent video games aggressive behavior. full original data can found https://github.com/Joe-Hilgard/Anderson-meta.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Anderson2010.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria — Anderson2010","text":"","code":"Anderson2010"},{"path":"https://https://fbartos.github.io/RoBMA/reference/Anderson2010.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria — Anderson2010","text":"data.frame 3 columns 23 observations.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Anderson2010.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"27 experimental studies from anderson2010violent;textualRoBMA that meet the best practice criteria — Anderson2010","text":"data.frame.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/Andrews2021.html","id":null,"dir":"Reference","previous_headings":"","what":"36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA — Andrews2021","title":"36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA — Andrews2021","text":"data set contains correlation coefficients r, standard errors se, executive functioning assessment type measure, mean age children study age. original data set assessed effect household chaos child executive functions andrews2021examiningRoBMA used example bartos2020adjusting;textualRoBMA.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Andrews2021.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA — Andrews2021","text":"","code":"Andrews2021"},{"path":"https://https://fbartos.github.io/RoBMA/reference/Andrews2021.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA — Andrews2021","text":"data.frame 4 columns 36 observations.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Andrews2021.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by andrews2021examining;textualRoBMA — Andrews2021","text":"data.frame.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/Bem2011.html","id":null,"dir":"Reference","previous_headings":"","what":"9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA — Bem2011","title":"9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA — Bem2011","text":"data set contains Cohen's d effect sizes, standard errors, labels 9 experimental studies precognition infamous bem2011feeling;textualRoBMA analyzed later meta-analysis bem2011mustRoBMA.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Bem2011.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA — Bem2011","text":"","code":"Bem2011"},{"path":"https://https://fbartos.github.io/RoBMA/reference/Bem2011.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA — Bem2011","text":"data.frame 3 columns 9 observations.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Bem2011.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"9 experimental studies from bem2011feeling;textualRoBMA as described in bem2011must;textualRoBMA — Bem2011","text":"data.frame.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/BiBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA","title":"Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA","text":"BiBMA estimate binomial-normal Bayesian model-averaged meta-analysis. interface allows complete customization ensemble different prior (list prior) distributions component.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/BiBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA","text":"","code":"BiBMA( x1, x2, n1, n2, study_names = NULL, study_ids = NULL, priors_effect = prior(distribution = \"student\", parameters = list(location = 0, scale = 0.58, df = 4)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1.77, scale = 0.55)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_baseline = NULL, priors_baseline_null = prior_factor(\"beta\", parameters = list(alpha = 1, beta = 1), contrast = \"independent\"), chains = 3, sample = 5000, burnin = 2000, adapt = 500, thin = 1, parallel = FALSE, autofit = TRUE, autofit_control = set_autofit_control(), convergence_checks = set_convergence_checks(), save = \"all\", seed = NULL, silent = TRUE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/BiBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA","text":"x1 vector number successes first group x2 vector number successes second group n1 vector number observations first group n2 vector number observations second group study_names optional argument names studies study_ids optional argument specifying dependency studies (using multilevel model). Defaults NULL studies independent. priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"student\", parameters = list(location = 0, scale = 0.58, df = 4)), based logOR meta-analytic estimates Cochrane Database Systematic Reviews bartos2023empiricalRoBMA. priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1.77, scale = 0.55)) based heterogeneities logOR estimates Cochrane Database Systematic Reviews bartos2023empiricalRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_baseline prior distributions alternative hypothesis intercepts (pi) study. Defaults NULL. priors_baseline_null prior distributions null hypothesis intercepts (pi) study. Defaults independent uniform prior distribution intercept prior(\"beta\", parameters = list(alpha = 1, beta = 1), contrast = \"independent\"). chains number chains MCMC algorithm. sample number sampling iterations MCMC algorithm. Defaults 5000. burnin number burnin iterations MCMC algorithm. Defaults 2000. adapt number adaptation iterations MCMC algorithm. Defaults 500. thin thinning chains MCMC algorithm. Defaults 1. parallel whether individual models fitted parallel. Defaults FALSE. implementation completely stable might cause connection error. autofit whether model fitted convergence criteria (specified autofit_control) satisfied. Defaults TRUE. autofit_control allows pass autofit control settings set_autofit_control() function. See ?set_autofit_control options default settings. convergence_checks automatic convergence checks assess fitted models, passed set_convergence_checks() function. See ?set_convergence_checks options default settings. save whether models posterior distributions kept obtaining model-averaged result. Defaults \"\" remove anything. Set \"min\" significantly reduce size final object, however, model diagnostics manipulation object possible. seed seed set model fitting, marginal likelihood computation, posterior mixing reproducibility results. Defaults NULL - seed set. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/BiBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA","text":"NoBMA returns object class 'RoBMA'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/BiBMA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA","text":"BiBMA() function estimates binomial-normal Bayesian model-averaged meta-analysis described bartos2023empirical;textualRoBMA. See vignette(\"MedicineBiBMA\", package = \"RoBMA\") vignette reproduction oduwole2018honey;textualRoBMA example. Also RoBMA() additional details. Generic summary.RoBMA(), print.RoBMA(), plot.RoBMA() functions provided facilitate manipulation ensemble. visual check individual model diagnostics can obtained using diagnostics() function. fitted model can updated modified update.RoBMA() function.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/BiBMA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data — BiBMA","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Oduwole (2018) and reproducing the example from # Bartos et al. (2023) with domain specific informed prior distributions fit <- BiBMA( x1 = c(5, 2), x2 = c(0, 0), n1 = c(35, 40), n2 = c(39, 40), priors_effect = prior_informed( \"Acute Respiratory Infections\", type = \"logOR\", parameter = \"effect\"), priors_heterogeneity = prior_informed( \"Acute Respiratory Infections\", type = \"logOR\", parameter = \"heterogeneity\") ) summary(fit) # produce summary on OR scale summary(fit, output_scale = \"OR\") } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Check fitted RoBMA object for errors and warnings — check_RoBMA","title":"Check fitted RoBMA object for errors and warnings — check_RoBMA","text":"Checks fitted RoBMA object warnings errors prints console.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check fitted RoBMA object for errors and warnings — check_RoBMA","text":"","code":"check_RoBMA(fit) check_RoBMA_convergence(fit)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check fitted RoBMA object for errors and warnings — check_RoBMA","text":"fit fitted RoBMA object.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check fitted RoBMA object for errors and warnings — check_RoBMA","text":"check_RoBMA returns vector error warning messages. check_RoBMA_convergence returns logical vector indicating whether models converged.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.BiBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints summary of ","title":"Prints summary of ","text":"check_setup prints summary \"RoBMA.reg\" ensemble implied specified prior distributions. useful checking ensemble configuration prior fitting models.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.BiBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints summary of ","text":"","code":"check_setup.BiBMA( priors_effect = prior(distribution = \"student\", parameters = list(location = 0, scale = 0.58, df = 4)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1.77, scale = 0.55)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_baseline = NULL, priors_baseline_null = prior_factor(\"beta\", parameters = list(alpha = 1, beta = 1), contrast = \"independent\"), models = FALSE, silent = FALSE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.BiBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints summary of ","text":"priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"student\", parameters = list(location = 0, scale = 0.58, df = 4)), based logOR meta-analytic estimates Cochrane Database Systematic Reviews bartos2023empiricalRoBMA. priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1.77, scale = 0.55)) based heterogeneities logOR estimates Cochrane Database Systematic Reviews bartos2023empiricalRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_baseline prior distributions alternative hypothesis intercepts (pi) study. Defaults NULL. priors_baseline_null prior distributions null hypothesis intercepts (pi) study. Defaults independent uniform prior distribution intercept prior(\"beta\", parameters = list(alpha = 1, beta = 1), contrast = \"independent\"). models models' details printed. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.BiBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints summary of ","text":"check_setup.reg invisibly returns list summary tables.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints summary of ","title":"Prints summary of ","text":"check_setup prints summary \"RoBMA\" ensemble implied specified prior distributions. useful checking ensemble configuration prior fitting models.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints summary of ","text":"","code":"check_setup( model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = list(prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_bias_null = prior_none(), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, models = FALSE, silent = FALSE ) check_setup.RoBMA( model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = list(prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_bias_null = prior_none(), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, models = FALSE, silent = FALSE )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints summary of ","text":"model_type string specifying RoBMA ensemble. Defaults NULL. options \"PSMA\", \"PP\", \"2w\" override settings passed priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, priors_effect. See details information different model types. priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults standard normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)). priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = .15)) based heterogeneities estimates psychology erp2017estimatesRoBMA. priors_bias list prior distributions publication bias adjustment component treated belonging alternative hypothesis. Defaults list( prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) ), corresponding RoBMA-PSMA model introduce bartos2021no;textualRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_bias_null list prior weight functions omega parameter treated belonging null hypothesis. Defaults publication bias adjustment, prior_none(). priors_hierarchical list prior distributions correlation random effects (rho) parameter treated belonging alternative hypothesis. setting allows users fit hierarchical (three-level) meta-analysis study_ids supplied. Note experimental feature see News details. Defaults beta distribution prior(distribution = \"beta\", parameters = list(alpha = 1, beta = 1)). priors_hierarchical_null list prior distributions correlation random effects (rho) parameter treated belonging null hypothesis. Defaults NULL. models models' details printed. silent print results.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints summary of ","text":"check_setup invisibly returns list summary tables.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.reg.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints summary of ","title":"Prints summary of ","text":"check_setup prints summary \"RoBMA.reg\" ensemble implied specified prior distributions. useful checking ensemble configuration prior fitting models. check_setup prints summary \"RoBMA.reg\" ensemble implied specified prior distributions. useful checking ensemble configuration prior fitting models.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.reg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints summary of ","text":"","code":"check_setup.reg( formula, data, test_predictors = TRUE, study_names = NULL, study_ids = NULL, transformation = if (any(colnames(data) != \"y\")) \"fishers_z\" else \"none\", prior_scale = if (any(colnames(data) != \"y\")) \"cohens_d\" else \"none\", standardize_predictors = TRUE, effect_direction = \"positive\", priors = NULL, model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = list(prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_bias_null = prior_none(), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, prior_covariates = prior(\"normal\", parameters = list(mean = 0, sd = 0.25)), prior_covariates_null = prior(\"spike\", parameters = list(location = 0)), prior_factors = prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"), prior_factors_null = prior(\"spike\", parameters = list(location = 0)), models = FALSE, silent = FALSE, ... ) check_setup.RoBMA.reg( formula, data, test_predictors = TRUE, study_names = NULL, study_ids = NULL, transformation = if (any(colnames(data) != \"y\")) \"fishers_z\" else \"none\", prior_scale = if (any(colnames(data) != \"y\")) \"cohens_d\" else \"none\", standardize_predictors = TRUE, effect_direction = \"positive\", priors = NULL, model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = list(prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_bias_null = prior_none(), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, prior_covariates = prior(\"normal\", parameters = list(mean = 0, sd = 0.25)), prior_covariates_null = prior(\"spike\", parameters = list(location = 0)), prior_factors = prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"), prior_factors_null = prior(\"spike\", parameters = list(location = 0)), models = FALSE, silent = FALSE, ... ) check_setup.reg( formula, data, test_predictors = TRUE, study_names = NULL, study_ids = NULL, transformation = if (any(colnames(data) != \"y\")) \"fishers_z\" else \"none\", prior_scale = if (any(colnames(data) != \"y\")) \"cohens_d\" else \"none\", standardize_predictors = TRUE, effect_direction = \"positive\", priors = NULL, model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = list(prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_bias_null = prior_none(), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, prior_covariates = prior(\"normal\", parameters = list(mean = 0, sd = 0.25)), prior_covariates_null = prior(\"spike\", parameters = list(location = 0)), prior_factors = prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"), prior_factors_null = prior(\"spike\", parameters = list(location = 0)), models = FALSE, silent = FALSE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.reg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints summary of ","text":"formula formula meta-regression model data data.frame containing data meta-regression. Note column names correspond effect sizes (d, logOR, , r, z), measure sampling variability (se, v, n, lCI, uCI, t), predictors. See combine_data() complete list reserved names additional information specifying input data. test_predictors vector predictor names test presence moderation (.e., assigned null alternative prior distributions). Defaults TRUE, predictors tested using default prior distributions (.e., prior_covariates, prior_covariates_null, prior_factors, prior_factors_null). estimate adjust effect predictors use FALSE. priors specified, settings test_predictors overridden. study_names optional argument names studies study_ids optional argument specifying dependency studies (using multilevel model). Defaults NULL studies independent. transformation transformation applied supplied effect sizes fitting individual models. Defaults \"fishers_z\". highly recommend using \"fishers_z\" transformation since variance stabilizing measure bias PET PEESE style models. options \"cohens_d\", correlation coefficient \"r\" \"logOR\". Supplying \"none\" treat effect sizes unstandardized refrain transformations. prior_scale effect size scale used define priors. Defaults \"cohens_d\". options \"fishers_z\", correlation coefficient \"r\", \"logOR\". prior scale need match effect sizes measure - samples prior distributions internally transformed match transformation data. prior_scale corresponds effect size scale default output, can changed within summary function. standardize_predictors whether continuous predictors standardized prior estimating model. Defaults TRUE. effect_direction expected direction effect. Correctly specifying expected direction effect crucial one-sided selection models, specify cut-offs using one-sided p-values. Defaults \"positive\" (another option \"negative\"). priors named list prior distributions predictor (names corresponding predictors). allows users specify null alternative hypothesis prior distributions predictor assigning corresponding element named list another named list (\"null\" \"alt\"). one prior specified given parameter, assumed correspond alternative hypotheses default null hypothesis specified (.e., prior_covariates_null prior_factors_null). named list one named prior distribution provided (either \"null\" \"alt\"), prior distribution used default distribution filled . Parameters without specified prior distributions assumed adjusted using default alternative hypothesis prior distributions (.e., prior_covariates prior_factors). priors specified, test_predictors ignored. model_type string specifying RoBMA ensemble. Defaults NULL. options \"PSMA\", \"PP\", \"2w\" override settings passed priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, priors_effect. See details information different model types. priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults standard normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)). priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = .15)) based heterogeneities estimates psychology erp2017estimatesRoBMA. priors_bias list prior distributions publication bias adjustment component treated belonging alternative hypothesis. Defaults list( prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) ), corresponding RoBMA-PSMA model introduce bartos2021no;textualRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_bias_null list prior weight functions omega parameter treated belonging null hypothesis. Defaults publication bias adjustment, prior_none(). priors_hierarchical list prior distributions correlation random effects (rho) parameter treated belonging alternative hypothesis. setting allows users fit hierarchical (three-level) meta-analysis study_ids supplied. Note experimental feature see News details. Defaults beta distribution prior(distribution = \"beta\", parameters = list(alpha = 1, beta = 1)). priors_hierarchical_null list prior distributions correlation random effects (rho) parameter treated belonging null hypothesis. Defaults NULL. prior_covariates prior distributions regression parameter continuous covariates effect size alternative hypothesis (unless set explicitly priors). Defaults relatively wide normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 0.25)). prior_covariates_null prior distributions regression parameter continuous covariates effect size null hypothesis (unless set explicitly priors). Defaults effect prior(\"spike\", parameters = list(location = 0)). prior_factors prior distributions regression parameter categorical covariates effect size alternative hypothesis (unless set explicitly priors). Defaults relatively wide multivariate normal distribution specifying differences mean contrasts prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"). prior_factors_null prior distributions regression parameter categorical covariates effect size null hypothesis (unless set explicitly priors). Defaults effect prior(\"spike\", parameters = list(location = 0)). models models' details printed. silent print results. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/check_setup.reg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints summary of ","text":"check_setup.reg invisibly returns list summary tables. check_setup.reg invisibly returns list summary tables.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/combine_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Combines different effect sizes into a common metric — combine_data","title":"Combines different effect sizes into a common metric — combine_data","text":"combine_data combines different effect sizes common measure specified transformation. Either data.frame data columns named corresponding arguments vectors individual values can passed.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/combine_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combines different effect sizes into a common metric — combine_data","text":"","code":"combine_data( d = NULL, r = NULL, z = NULL, logOR = NULL, OR = NULL, t = NULL, y = NULL, se = NULL, v = NULL, n = NULL, lCI = NULL, uCI = NULL, study_names = NULL, study_ids = NULL, weight = NULL, data = NULL, transformation = \"fishers_z\", return_all = FALSE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/combine_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combines different effect sizes into a common metric — combine_data","text":"d vector effect sizes measured Cohen's d r vector effect sizes measured correlations z vector effect sizes measured Fisher's z logOR vector effect sizes measured log odds ratios vector effect sizes measured odds ratios t vector t/z-statistics y vector unspecified effect sizes (note effect size transformations unavailable type input) se vector standard errors effect sizes v vector variances effect sizes n vector overall sample sizes lCI vector lower bounds confidence intervals uCI vector upper bounds confidence intervals study_names optional argument names studies study_ids optional argument specifying dependency studies (using multilevel model). Defaults NULL studies independent. weight specifies likelihood weights individual estimates. Notes untested experimental feature. data data frame column names corresponding variable names used supply data individually transformation transformation applied supplied effect sizes fitting individual models. Defaults \"fishers_z\". highly recommend using \"fishers_z\" transformation since variance stabilizing measure bias PET PEESE style models. options \"cohens_d\", correlation coefficient \"r\" \"logOR\". Supplying \"none\" treat effect sizes unstandardized refrain transformations. return_all whether data frame containing filled values returned. Defaults FALSE ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/combine_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combines different effect sizes into a common metric — combine_data","text":"combine_data returns data.frame.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/combine_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Combines different effect sizes into a common metric — combine_data","text":"aim function combine different, already calculated, effect size measures. order obtain effect size measures raw values, e.g, mean differences, standard deviations, sample sizes, use escalc function. function checks input values transforming input common effect size measure following fashion: obtains missing standard errors squaring variances obtains missing standard errors confidence intervals (transformation Fisher's z scale d r). obtains missing sample sizes (standard errors logOR) t-statistics effect sizes obtains missing standard errors sample sizes effect sizes obtains missing sample sizes standard errors effect sizes obtains missing t-statistics sample sizes effect sizes (standard errors effect sizes logOR) changes effect sizes direction positive transforms effect sizes common effect size transforms standard errors common metric transforms NULL unstandardized effect size y supplied, steps 4-9 skipped.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.independent.html","id":null,"dir":"Reference","previous_headings":"","what":"Independent contrast matrix — contr.independent","title":"Independent contrast matrix — contr.independent","text":"Return matrix independent contrasts – level term.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.independent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Independent contrast matrix — contr.independent","text":"","code":"contr.independent(n, contrasts = TRUE)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.independent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Independent contrast matrix — contr.independent","text":"n vector levels factor, number levels contrasts logical indicating whether contrasts computed","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.independent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Independent contrast matrix — contr.independent","text":"matrix n rows k columns, k = n contrasts = TRUE k = n contrasts = FALSE.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.independent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Independent contrast matrix — contr.independent","text":"","code":"contr.independent(c(1, 2)) #> [,1] [,2] #> [1,] 1 0 #> [2,] 0 1 contr.independent(c(1, 2, 3)) #> [,1] [,2] [,3] #> [1,] 1 0 0 #> [2,] 0 1 0 #> [3,] 0 0 1"},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.meandif.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean difference contrast matrix — contr.meandif","title":"Mean difference contrast matrix — contr.meandif","text":"Return matrix mean difference contrasts. adjustment contr.orthonormal ascertains prior distributions difference gran mean factor level identical independent number factor levels (hold orthonormal contrast). Furthermore, contrast re-scaled specified prior distribution exactly corresponds prior distribution difference factor level grand mean – approximately twice scale contr.orthonormal.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.meandif.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean difference contrast matrix — contr.meandif","text":"","code":"contr.meandif(n, contrasts = TRUE)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.meandif.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean difference contrast matrix — contr.meandif","text":"n vector levels factor, number levels contrasts logical indicating whether contrasts computed","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.meandif.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean difference contrast matrix — contr.meandif","text":"matrix n rows k columns, k = n - 1 contrasts = TRUE k = n contrasts = FALSE.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.meandif.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean difference contrast matrix — contr.meandif","text":"","code":"contr.meandif(c(1, 2)) #> [,1] #> [1,] -1 #> [2,] 1 contr.meandif(c(1, 2, 3)) #> [,1] [,2] #> [1,] 0.0000000 1.0 #> [2,] -0.8660254 -0.5 #> [3,] 0.8660254 -0.5"},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.orthonormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthornomal contrast matrix — contr.orthonormal","title":"Orthornomal contrast matrix — contr.orthonormal","text":"Return matrix orthornomal contrasts. Code based stanova::contr.bayes corresponding description rouder2012default;textualBayesTools","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.orthonormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthornomal contrast matrix — contr.orthonormal","text":"","code":"contr.orthonormal(n, contrasts = TRUE)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.orthonormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthornomal contrast matrix — contr.orthonormal","text":"n vector levels factor, number levels contrasts logical indicating whether contrasts computed","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.orthonormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthornomal contrast matrix — contr.orthonormal","text":"matrix n rows k columns, k = n - 1 contrasts = TRUE k = n contrasts = FALSE.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/contr.orthonormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthornomal contrast matrix — contr.orthonormal","text":"","code":"contr.orthonormal(c(1, 2)) #> [,1] #> [1,] -0.7071068 #> [2,] 0.7071068 contr.orthonormal(c(1, 2, 3)) #> [,1] [,2] #> [1,] 0.0000000 0.8164966 #> [2,] -0.7071068 -0.4082483 #> [3,] 0.7071068 -0.4082483"},{"path":"https://https://fbartos.github.io/RoBMA/reference/diagnostics.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks a fitted RoBMA object — diagnostics","title":"Checks a fitted RoBMA object — diagnostics","text":"diagnostics creates visual checks individual models convergence. Numerical overview individual models can obtained summary(object, type = \"models\", diagnostics = TRUE), even detailed information summary(object, type = \"individual\").","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/diagnostics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks a fitted RoBMA object — diagnostics","text":"","code":"diagnostics( fit, parameter, type, plot_type = \"base\", show_models = NULL, lags = 30, title = is.null(show_models) | length(show_models) > 1, ... ) diagnostics_autocorrelation( fit, parameter = NULL, plot_type = \"base\", show_models = NULL, lags = 30, title = is.null(show_models) | length(show_models) > 1, ... ) diagnostics_trace( fit, parameter = NULL, plot_type = \"base\", show_models = NULL, title = is.null(show_models) | length(show_models) > 1, ... ) diagnostics_density( fit, parameter = NULL, plot_type = \"base\", show_models = NULL, title = is.null(show_models) | length(show_models) > 1, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/diagnostics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks a fitted RoBMA object — diagnostics","text":"fit fitted RoBMA object parameter parameter plotted. Either \"mu\", \"tau\", \"omega\", \"PET\", \"PEESE\". type type MCMC diagnostic plotted. Options \"chains\" chains' trace plots, \"autocorrelation\" autocorrelation chains, \"densities\" overlaying densities individual chains. Can abbreviated first letters. plot_type whether use base plot \"base\" ggplot2 \"ggplot\" plotting. Defaults \"base\". show_models MCMC diagnostics models plotted. Defaults NULL plots MCMC diagnostics specified parameter every model part ensemble. lags number lags shown type = \"autocorrelation\". Defaults 30. title whether model number displayed title. Defaults TRUE one model selected. ... additional arguments passed par plot_type = \"base\".","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/diagnostics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks a fitted RoBMA object — diagnostics","text":"diagnostics returns either NULL plot_type = \"base\" object/list objects (depending number parameters plotted) class 'ggplot2' plot_type = \"ggplot2\".","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/diagnostics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Checks a fitted RoBMA object — diagnostics","text":"visualization functions based stan_plot function color schemes.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/diagnostics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Checks a fitted RoBMA object — diagnostics","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Anderson et al. 2010 and fitting the default model # (note that the model can take a while to fit) fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels) ### ggplot2 version of all of the plots can be obtained by adding 'model_type = \"ggplot\" # diagnostics function allows to visualize diagnostics of a fitted RoBMA object, for example, # the trace plot for the mean parameter in each model model diagnostics(fit, parameter = \"mu\", type = \"chain\") # in order to show the trace plot only for the 11th model, add show_models parameter diagnostics(fit, parameter = \"mu\", type = \"chain\", show_models = 11) # furthermore, the autocorrelations diagnostics(fit, parameter = \"mu\", type = \"autocorrelation\") # and overlying densities for each plot can also be visualize diagnostics(fit, parameter = \"mu\", type = \"densities\") } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/effect_sizes.html","id":null,"dir":"Reference","previous_headings":"","what":"Effect size transformations — effect_sizes","title":"Effect size transformations — effect_sizes","text":"Functions transforming different effect size measures.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/effect_sizes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Effect size transformations — effect_sizes","text":"","code":"d2r(d) d2z(d) d2logOR(d) d2OR(d) r2d(r) r2z(r) r2logOR(r) r2OR(r) z2r(z) z2d(z) z2logOR(z) z2OR(z) logOR2r(logOR) logOR2z(logOR) logOR2d(logOR) logOR2OR(logOR) OR2r(OR) OR2z(OR) OR2logOR(OR) OR2d(OR)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/effect_sizes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Effect size transformations — effect_sizes","text":"d Cohen's d. r correlation coefficient. z Fisher's z. logOR log(odds ratios). offs ratios.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/effect_sizes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Effect size transformations — effect_sizes","text":"transformations based borenstein2011introductionRoBMA. case direct transformation available, transformations chained provide effect size interest.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/forest.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest plot for a RoBMA object — forest","title":"Forest plot for a RoBMA object — forest","text":"forest creates forest plot \"RoBMA\" object.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/forest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest plot for a RoBMA object — forest","text":"","code":"forest( x, conditional = FALSE, plot_type = \"base\", output_scale = NULL, order = NULL, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/forest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest plot for a RoBMA object — forest","text":"x fitted RoBMA object conditional whether conditional estimates plotted. Defaults FALSE plots model-averaged estimates. Note \"weightfunction\" \"PET-PEESE\" always ignoring type publication bias adjustment. plot_type whether use base plot \"base\" ggplot2 \"ggplot\" plotting. Defaults \"base\". output_scale transform effect sizes meta-analytic effect size estimate different scale. Defaults NULL returns scale model estimated . order order studies. Defaults NULL - ordering supplied fitting function. Studies can ordered either \"increasing\" \"decreasing\" effect size, labels \"alphabetical\". ... list additional graphical arguments passed plotting function. Supported arguments lwd, lty, col, col.fill, xlab, ylab, main, xlim, ylim adjust line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, y-axis range respectively.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/forest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest plot for a RoBMA object — forest","text":"forest returns either NULL plot_type = \"base\" object object class 'ggplot2' plot_type = \"ggplot2\".","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/forest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest plot for a RoBMA object — forest","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Anderson et al. 2010 and fitting the default model # (note that the model can take a while to fit) fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels) ### ggplot2 version of all of the plots can be obtained by adding 'model_type = \"ggplot\" # the forest function creates a forest plot for a fitted RoBMA object, for example, # the forest plot for the individual studies and the model-averaged effect size estimate forest(fit) # the conditional effect size estimate forest(fit, conditional = TRUE) # or transforming the effect size estimates to Fisher's z forest(fit, output_scale = \"fishers_z\") } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/interpret.html","id":null,"dir":"Reference","previous_headings":"","what":"Interprets results of a RoBMA model. — interpret","title":"Interprets results of a RoBMA model. — interpret","text":"interpret creates brief textual summary fitted RoBMA object.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/interpret.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interprets results of a RoBMA model. — interpret","text":"","code":"interpret(object, output_scale = NULL)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/interpret.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interprets results of a RoBMA model. — interpret","text":"object fitted RoBMA object output_scale transform meta-analytic estimates different scale. Defaults NULL returns scale model estimated .","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/interpret.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interprets results of a RoBMA model. — interpret","text":"interpret returns character.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/is.RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Reports whether x is a RoBMA object — is.RoBMA","title":"Reports whether x is a RoBMA object — is.RoBMA","text":"Reports whether x RoBMA object","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/is.RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reports whether x is a RoBMA object — is.RoBMA","text":"","code":"is.RoBMA(x) is.RoBMA.reg(x) is.NoBMA(x) is.NoBMA.reg(x) is.BiBMA(x)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/is.RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reports whether x is a RoBMA object — is.RoBMA","text":"x object test","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/is.RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reports whether x is a RoBMA object — is.RoBMA","text":"returns boolean.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Kroupova2021.html","id":null,"dir":"Reference","previous_headings":"","what":"881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA — Kroupova2021","title":"881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA — Kroupova2021","text":"data set contains partial correlation coefficients, standard errors, study labels, samples sizes, type educational outcome, intensity employment, gender student population, study location, study design, whether study controlled endogenity, whether study controlled motivation. original data set including additional variables publication can found http://meta-analysis.cz/students. (Note standard errors employment intensities missing.)","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Kroupova2021.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA — Kroupova2021","text":"","code":"Kroupova2021"},{"path":"https://https://fbartos.github.io/RoBMA/reference/Kroupova2021.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA — Kroupova2021","text":"data.frame 11 columns 881 observations.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Kroupova2021.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"881 estimates from 69 studies of a relationship between employment and educational outcomes collected by kroupova2021student;textualRoBMA — Kroupova2021","text":"data.frame.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/Lui2015.html","id":null,"dir":"Reference","previous_headings":"","what":"18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA — Lui2015","title":"18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA — Lui2015","text":"data set contains correlation coefficients r, sample sizes n, labels study assessing relationship acculturation mismatch (result contrast collectivist cultures Asian Latin immigrant groups individualist culture United States) intergenerational cultural conflict lui2015intergenerationalRoBMA used example bartos2020adjusting;textualRoBMA.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Lui2015.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA — Lui2015","text":"","code":"Lui2015"},{"path":"https://https://fbartos.github.io/RoBMA/reference/Lui2015.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA — Lui2015","text":"data.frame 3 columns 18 observations.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Lui2015.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by lui2015intergenerational;textualRoBMA — Lui2015","text":"data.frame.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots marginal estimates of a fitted RoBMA regression object — marginal_plot","title":"Plots marginal estimates of a fitted RoBMA regression object — marginal_plot","text":"marginal_plot allows visualize prior posterior distributions marginal estimates RoBMA regression model.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots marginal estimates of a fitted RoBMA regression object — marginal_plot","text":"","code":"marginal_plot( x, parameter, conditional = FALSE, plot_type = \"base\", prior = FALSE, output_scale = NULL, dots_prior = NULL, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots marginal estimates of a fitted RoBMA regression object — marginal_plot","text":"x fitted RoBMA regression object parameter regression parameter plotted conditional whether conditional marginal estimates plotted. Defaults FALSE plots model-averaged estimates. plot_type whether use base plot \"base\" ggplot2 \"ggplot\" plotting. Defaults \"base\". prior whether prior distribution added figure. Defaults FALSE. output_scale transform effect sizes meta-analytic effect size estimate different scale. Defaults NULL returns scale model estimated . dots_prior list additional graphical arguments passed plotting function prior distribution. Supported arguments lwd, lty, col, col.fill, adjust line thickness, line type, line color, fill color prior distribution respectively. ... list additional graphical arguments passed plotting function. Supported arguments lwd, lty, col, col.fill, xlab, ylab, main, xlim, ylim adjust line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, y-axis range respectively.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots marginal estimates of a fitted RoBMA regression object — marginal_plot","text":"plot.RoBMA returns either NULL plot_type = \"base\" object object class 'ggplot2' plot_type = \"ggplot2\".","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize marginal estimates of a fitted RoBMA regression object — marginal_summary","title":"Summarize marginal estimates of a fitted RoBMA regression object — marginal_summary","text":"marginal_summary creates summary tables marginal estimates RoBMA regression model.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize marginal estimates of a fitted RoBMA regression object — marginal_summary","text":"","code":"marginal_summary( object, conditional = FALSE, output_scale = NULL, probs = c(0.025, 0.975), logBF = FALSE, BF01 = FALSE )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize marginal estimates of a fitted RoBMA regression object — marginal_summary","text":"object fitted RoBMA regression object conditional show conditional estimates (assuming alternative true). output_scale transform meta-analytic estimates different scale. Defaults NULL returns scale model estimated . probs quantiles posterior samples displayed. Defaults c(.025, .975) logBF show log Bayes factors. Defaults FALSE. BF01 show Bayes factors support null hypotheses. Defaults FALSE.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/marginal_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize marginal estimates of a fitted RoBMA regression object — marginal_summary","text":"marginal_summary returns list tables class 'BayesTools_table'.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a Bayesian Model-Averaged Meta-Analysis — NoBMA","title":"Estimate a Bayesian Model-Averaged Meta-Analysis — NoBMA","text":"NoBMA wrapper around RoBMA() can used estimate publication bias unadjusted Bayesian model-averaged meta-analysis. interface allows complete customization ensemble different prior (list prior) distributions component.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a Bayesian Model-Averaged Meta-Analysis — NoBMA","text":"","code":"NoBMA( d = NULL, r = NULL, logOR = NULL, OR = NULL, z = NULL, y = NULL, se = NULL, v = NULL, n = NULL, lCI = NULL, uCI = NULL, t = NULL, study_names = NULL, study_ids = NULL, data = NULL, weight = NULL, transformation = if (is.null(y)) \"fishers_z\" else \"none\", prior_scale = if (is.null(y)) \"cohens_d\" else \"none\", model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, chains = 3, sample = 5000, burnin = 2000, adapt = 500, thin = 1, parallel = FALSE, autofit = TRUE, autofit_control = set_autofit_control(), convergence_checks = set_convergence_checks(), save = \"all\", seed = NULL, silent = TRUE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a Bayesian Model-Averaged Meta-Analysis — NoBMA","text":"d vector effect sizes measured Cohen's d r vector effect sizes measured correlations logOR vector effect sizes measured log odds ratios vector effect sizes measured odds ratios z vector effect sizes measured Fisher's z y vector unspecified effect sizes (note effect size transformations unavailable type input) se vector standard errors effect sizes v vector variances effect sizes n vector overall sample sizes lCI vector lower bounds confidence intervals uCI vector upper bounds confidence intervals t vector t/z-statistics study_names optional argument names studies study_ids optional argument specifying dependency studies (using multilevel model). Defaults NULL studies independent. data data object created combine_data function. alternative input entry specifying d, r, y, etc... directly. .e., RoBMA function allow passing data.frame referencing columns. weight specifies likelihood weights individual estimates. Notes untested experimental feature. transformation transformation applied supplied effect sizes fitting individual models. Defaults \"fishers_z\". highly recommend using \"fishers_z\" transformation since variance stabilizing measure bias PET PEESE style models. options \"cohens_d\", correlation coefficient \"r\" \"logOR\". Supplying \"none\" treat effect sizes unstandardized refrain transformations. prior_scale effect size scale used define priors. Defaults \"cohens_d\". options \"fishers_z\", correlation coefficient \"r\", \"logOR\". prior scale need match effect sizes measure - samples prior distributions internally transformed match transformation data. prior_scale corresponds effect size scale default output, can changed within summary function. model_type string specifying RoBMA ensemble. Defaults NULL. options \"PSMA\", \"PP\", \"2w\" override settings passed priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, priors_effect. See details information different model types. priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults standard normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)). priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = .15)) based heterogeneities estimates psychology erp2017estimatesRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_hierarchical list prior distributions correlation random effects (rho) parameter treated belonging alternative hypothesis. setting allows users fit hierarchical (three-level) meta-analysis study_ids supplied. Note experimental feature see News details. Defaults beta distribution prior(distribution = \"beta\", parameters = list(alpha = 1, beta = 1)). priors_hierarchical_null list prior distributions correlation random effects (rho) parameter treated belonging null hypothesis. Defaults NULL. chains number chains MCMC algorithm. sample number sampling iterations MCMC algorithm. Defaults 5000. burnin number burnin iterations MCMC algorithm. Defaults 2000. adapt number adaptation iterations MCMC algorithm. Defaults 500. thin thinning chains MCMC algorithm. Defaults 1. parallel whether individual models fitted parallel. Defaults FALSE. implementation completely stable might cause connection error. autofit whether model fitted convergence criteria (specified autofit_control) satisfied. Defaults TRUE. autofit_control allows pass autofit control settings set_autofit_control() function. See ?set_autofit_control options default settings. convergence_checks automatic convergence checks assess fitted models, passed set_convergence_checks() function. See ?set_convergence_checks options default settings. save whether models posterior distributions kept obtaining model-averaged result. Defaults \"\" remove anything. Set \"min\" significantly reduce size final object, however, model diagnostics manipulation object possible. seed seed set model fitting, marginal likelihood computation, posterior mixing reproducibility results. Defaults NULL - seed set. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a Bayesian Model-Averaged Meta-Analysis — NoBMA","text":"NoBMA returns object class 'RoBMA'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a Bayesian Model-Averaged Meta-Analysis — NoBMA","text":"See RoBMA() details. Note default prior distributions relatively wide informed prior distributions testing presence moderation considered.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.reg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a Bayesian Model-Averaged Meta-Regression — NoBMA.reg","title":"Estimate a Bayesian Model-Averaged Meta-Regression — NoBMA.reg","text":"NoBMA.reg wrapper around RoBMA.reg() can used estimate publication bias unadjusted Bayesian model-averaged meta-regression. interface allows complete customization ensemble different prior (list prior) distributions component.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.reg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a Bayesian Model-Averaged Meta-Regression — NoBMA.reg","text":"","code":"NoBMA.reg( formula, data, test_predictors = TRUE, study_names = NULL, study_ids = NULL, transformation = if (any(colnames(data) != \"y\")) \"fishers_z\" else \"none\", prior_scale = if (any(colnames(data) != \"y\")) \"cohens_d\" else \"none\", standardize_predictors = TRUE, priors = NULL, model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, prior_covariates = prior(\"normal\", parameters = list(mean = 0, sd = 0.25)), prior_covariates_null = prior(\"spike\", parameters = list(location = 0)), prior_factors = prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"), prior_factors_null = prior_factor(\"spike\", parameters = list(location = 0), contrast = \"meandif\"), chains = 3, sample = 5000, burnin = 2000, adapt = 500, thin = 1, parallel = FALSE, autofit = TRUE, autofit_control = set_autofit_control(), convergence_checks = set_convergence_checks(), save = \"all\", seed = NULL, silent = TRUE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.reg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a Bayesian Model-Averaged Meta-Regression — NoBMA.reg","text":"formula formula meta-regression model data data.frame containing data meta-regression. Note column names correspond effect sizes (d, logOR, , r, z), measure sampling variability (se, v, n, lCI, uCI, t), predictors. See combine_data() complete list reserved names additional information specifying input data. test_predictors vector predictor names test presence moderation (.e., assigned null alternative prior distributions). Defaults TRUE, predictors tested using default prior distributions (.e., prior_covariates, prior_covariates_null, prior_factors, prior_factors_null). estimate adjust effect predictors use FALSE. priors specified, settings test_predictors overridden. study_names optional argument names studies study_ids optional argument specifying dependency studies (using multilevel model). Defaults NULL studies independent. transformation transformation applied supplied effect sizes fitting individual models. Defaults \"fishers_z\". highly recommend using \"fishers_z\" transformation since variance stabilizing measure bias PET PEESE style models. options \"cohens_d\", correlation coefficient \"r\" \"logOR\". Supplying \"none\" treat effect sizes unstandardized refrain transformations. prior_scale effect size scale used define priors. Defaults \"cohens_d\". options \"fishers_z\", correlation coefficient \"r\", \"logOR\". prior scale need match effect sizes measure - samples prior distributions internally transformed match transformation data. prior_scale corresponds effect size scale default output, can changed within summary function. standardize_predictors whether continuous predictors standardized prior estimating model. Defaults TRUE. priors named list prior distributions predictor (names corresponding predictors). allows users specify null alternative hypothesis prior distributions predictor assigning corresponding element named list another named list (\"null\" \"alt\"). one prior specified given parameter, assumed correspond alternative hypotheses default null hypothesis specified (.e., prior_covariates_null prior_factors_null). named list one named prior distribution provided (either \"null\" \"alt\"), prior distribution used default distribution filled . Parameters without specified prior distributions assumed adjusted using default alternative hypothesis prior distributions (.e., prior_covariates prior_factors). priors specified, test_predictors ignored. model_type string specifying RoBMA ensemble. Defaults NULL. options \"PSMA\", \"PP\", \"2w\" override settings passed priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, priors_effect. See details information different model types. priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults standard normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)). priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = .15)) based heterogeneities estimates psychology erp2017estimatesRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_hierarchical list prior distributions correlation random effects (rho) parameter treated belonging alternative hypothesis. setting allows users fit hierarchical (three-level) meta-analysis study_ids supplied. Note experimental feature see News details. Defaults beta distribution prior(distribution = \"beta\", parameters = list(alpha = 1, beta = 1)). priors_hierarchical_null list prior distributions correlation random effects (rho) parameter treated belonging null hypothesis. Defaults NULL. prior_covariates prior distributions regression parameter continuous covariates effect size alternative hypothesis (unless set explicitly priors). Defaults relatively wide normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 0.25)). prior_covariates_null prior distributions regression parameter continuous covariates effect size null hypothesis (unless set explicitly priors). Defaults effect prior(\"spike\", parameters = list(location = 0)). prior_factors prior distributions regression parameter categorical covariates effect size alternative hypothesis (unless set explicitly priors). Defaults relatively wide multivariate normal distribution specifying differences mean contrasts prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"). prior_factors_null prior distributions regression parameter categorical covariates effect size null hypothesis (unless set explicitly priors). Defaults effect prior(\"spike\", parameters = list(location = 0)). chains number chains MCMC algorithm. sample number sampling iterations MCMC algorithm. Defaults 5000. burnin number burnin iterations MCMC algorithm. Defaults 2000. adapt number adaptation iterations MCMC algorithm. Defaults 500. thin thinning chains MCMC algorithm. Defaults 1. parallel whether individual models fitted parallel. Defaults FALSE. implementation completely stable might cause connection error. autofit whether model fitted convergence criteria (specified autofit_control) satisfied. Defaults TRUE. autofit_control allows pass autofit control settings set_autofit_control() function. See ?set_autofit_control options default settings. convergence_checks automatic convergence checks assess fitted models, passed set_convergence_checks() function. See ?set_convergence_checks options default settings. save whether models posterior distributions kept obtaining model-averaged result. Defaults \"\" remove anything. Set \"min\" significantly reduce size final object, however, model diagnostics manipulation object possible. seed seed set model fitting, marginal likelihood computation, posterior mixing reproducibility results. Defaults NULL - seed set. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.reg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a Bayesian Model-Averaged Meta-Regression — NoBMA.reg","text":"NoBMA.reg returns object class 'RoBMA'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/NoBMA.reg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a Bayesian Model-Averaged Meta-Regression — NoBMA.reg","text":"See RoBMA.reg() details. Note default prior distributions relatively wide informed prior distributions testing presence moderation considered.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot.RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots a fitted RoBMA object — plot.RoBMA","title":"Plots a fitted RoBMA object — plot.RoBMA","text":"plot.RoBMA allows visualize different \"RoBMA\" object parameters various ways. See type different model types.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot.RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots a fitted RoBMA object — plot.RoBMA","text":"","code":"# S3 method for class 'RoBMA' plot( x, parameter = \"mu\", conditional = FALSE, plot_type = \"base\", prior = FALSE, output_scale = NULL, rescale_x = FALSE, show_data = TRUE, dots_prior = NULL, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot.RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots a fitted RoBMA object — plot.RoBMA","text":"x fitted RoBMA object parameter parameter plotted. Defaults \"mu\" (effect size). additional options \"tau\" (heterogeneity), \"weightfunction\" (estimated weightfunction), \"PET-PEESE\" (PET-PEESE regression). conditional whether conditional estimates plotted. Defaults FALSE plots model-averaged estimates. Note \"weightfunction\" \"PET-PEESE\" always ignoring type publication bias adjustment. plot_type whether use base plot \"base\" ggplot2 \"ggplot\" plotting. Defaults \"base\". prior whether prior distribution added figure. Defaults FALSE. output_scale transform effect sizes meta-analytic effect size estimate different scale. Defaults NULL returns scale model estimated . rescale_x whether x-axis \"weightfunction\" re-scaled make x-ticks equally spaced. Defaults FALSE. show_data whether study estimates standard errors show \"PET-PEESE\" plot. Defaults TRUE. dots_prior list additional graphical arguments passed plotting function prior distribution. Supported arguments lwd, lty, col, col.fill, adjust line thickness, line type, line color, fill color prior distribution respectively. ... list additional graphical arguments passed plotting function. Supported arguments lwd, lty, col, col.fill, xlab, ylab, main, xlim, ylim adjust line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, y-axis range respectively.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot.RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots a fitted RoBMA object — plot.RoBMA","text":"plot.RoBMA returns either NULL plot_type = \"base\" object object class 'ggplot2' plot_type = \"ggplot2\".","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot.RoBMA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots a fitted RoBMA object — plot.RoBMA","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Anderson et al. 2010 and fitting the default model # (note that the model can take a while to fit) fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels) ### ggplot2 version of all of the plots can be obtained by adding 'model_type = \"ggplot\" # the 'plot' function allows to visualize the results of a fitted RoBMA object, for example; # the model-averaged effect size estimate plot(fit, parameter = \"mu\") # and show both the prior and posterior distribution plot(fit, parameter = \"mu\", prior = TRUE) # conditional plots can by obtained by specifying plot(fit, parameter = \"mu\", conditional = TRUE) # plotting function also allows to visualize the weight function plot(fit, parameter = \"weightfunction\") # re-scale the x-axis plot(fit, parameter = \"weightfunction\", rescale_x = TRUE) # or visualize the PET-PEESE regression line plot(fit, parameter = \"PET-PEESE\") } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot_models.html","id":null,"dir":"Reference","previous_headings":"","what":"Models plot for a RoBMA object — plot_models","title":"Models plot for a RoBMA object — plot_models","text":"plot_models plots individual models' estimates \"RoBMA\" object.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot_models.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Models plot for a RoBMA object — plot_models","text":"","code":"plot_models( x, parameter = \"mu\", conditional = FALSE, output_scale = NULL, plot_type = \"base\", order = \"decreasing\", order_by = \"model\", ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot_models.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Models plot for a RoBMA object — plot_models","text":"x fitted RoBMA object parameter parameter plotted. Defaults \"mu\" (effect size). additional option \"tau\" (heterogeneity). conditional whether conditional estimates plotted. Defaults FALSE plots model-averaged estimates. Note \"weightfunction\" \"PET-PEESE\" always ignoring type publication bias adjustment. output_scale transform effect sizes meta-analytic effect size estimate different scale. Defaults NULL returns scale model estimated . plot_type whether use base plot \"base\" ggplot2 \"ggplot\" plotting. Defaults \"base\". order models ordered. Defaults \"decreasing\" orders decreasing order accordance order_by argument. alternative \"increasing\". order_by feature use order models. Defaults \"model\" orders models according number. alternatives \"estimate\" (effect size estimates), \"probability\" (posterior model probability), \"BF\" (inclusion Bayes factor). ... list additional graphical arguments passed plotting function. Supported arguments lwd, lty, col, col.fill, xlab, ylab, main, xlim, ylim adjust line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, y-axis range respectively.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot_models.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Models plot for a RoBMA object — plot_models","text":"plot_models returns either NULL plot_type = \"base\" object object class 'ggplot2' plot_type = \"ggplot2\".","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/plot_models.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Models plot for a RoBMA object — plot_models","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Anderson et al. 2010 and fitting the default model # (note that the model can take a while to fit) fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels) ### ggplot2 version of all of the plots can be obtained by adding 'model_type = \"ggplot\" # the plot_models function creates a plot for of the individual models' estimates, for example, # the effect size estimates from the individual models can be obtained with plot_models(fit) # and effect size estimates from only the conditional models plot_models(fit, conditional = TRUE) } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/Poulsen2006.html","id":null,"dir":"Reference","previous_headings":"","what":"5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity — Poulsen2006","title":"5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity — Poulsen2006","text":"data set contains Cohen's d effect sizes, standard errors, labels 5 studies assessing tactile outcome meta-analysis effect potassium-containing toothpaste dentine hypersensitivity poulsen2006potassiumRoBMA used example bartos2021bayesian;textualRoBMA.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Poulsen2006.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity — Poulsen2006","text":"","code":"Poulsen2006"},{"path":"https://https://fbartos.github.io/RoBMA/reference/Poulsen2006.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity — Poulsen2006","text":"data.frame 3 columns 5 observations.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/Poulsen2006.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"5 studies with a tactile outcome assessment from poulsen2006potassium;textualRoBMA of the effect of potassium-containing toothpaste on dentine hypersensitivity — Poulsen2006","text":"data.frame.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.marginal_summary.RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints marginal_summary object for RoBMA method — print.marginal_summary.RoBMA","title":"Prints marginal_summary object for RoBMA method — print.marginal_summary.RoBMA","text":"Prints marginal_summary object RoBMA method","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.marginal_summary.RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints marginal_summary object for RoBMA method — print.marginal_summary.RoBMA","text":"","code":"# S3 method for class 'marginal_summary.RoBMA' print(x, ...)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.marginal_summary.RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints marginal_summary object for RoBMA method — print.marginal_summary.RoBMA","text":"x summary RoBMA object ... additional arguments","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.marginal_summary.RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints marginal_summary object for RoBMA method — print.marginal_summary.RoBMA","text":"print.marginal_summary.RoBMA invisibly returns print statement.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints a fitted RoBMA object — print.RoBMA","title":"Prints a fitted RoBMA object — print.RoBMA","text":"Prints fitted RoBMA object","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints a fitted RoBMA object — print.RoBMA","text":"","code":"# S3 method for class 'RoBMA' print(x, ...)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints a fitted RoBMA object — print.RoBMA","text":"x fitted RoBMA object. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints a fitted RoBMA object — print.RoBMA","text":"print.RoBMA invisibly returns print statement.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.summary.RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints summary object for RoBMA method — print.summary.RoBMA","title":"Prints summary object for RoBMA method — print.summary.RoBMA","text":"Prints summary object RoBMA method","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.summary.RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints summary object for RoBMA method — print.summary.RoBMA","text":"","code":"# S3 method for class 'summary.RoBMA' print(x, ...)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.summary.RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prints summary object for RoBMA method — print.summary.RoBMA","text":"x summary RoBMA object ... additional arguments","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/print.summary.RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prints summary object for RoBMA method — print.summary.RoBMA","text":"print.summary.RoBMA invisibly returns print statement.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a prior distribution — prior","title":"Creates a prior distribution — prior","text":"prior creates prior distribution. prior can visualized plot function.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a prior distribution — prior","text":"","code":"prior( distribution, parameters, truncation = list(lower = -Inf, upper = Inf), prior_weights = 1 )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a prior distribution — prior","text":"distribution name prior distribution. possible options \"point\" point density characterized location parameter. \"normal\" normal distribution characterized mean sd parameters. \"lognormal\" lognormal distribution characterized meanlog sdlog parameters. \"cauchy\" Cauchy distribution characterized location scale parameters. Internally converted generalized t-distribution df = 1. \"t\" generalized t-distribution characterized location, scale, df parameters. \"gamma\" gamma distribution characterized either shape rate, shape scale parameters. later internally converted shape rate parametrization \"invgamma\" inverse-gamma distribution characterized shape scale parameters. JAGS part uses 1/gamma distribution shape rate parameter. \"beta\" beta distribution characterized alpha beta parameters. \"exp\" exponential distribution characterized either rate scale parameter. later internally converted rate. \"uniform\" uniform distribution defined range b parameters list appropriate parameters given distribution. truncation list two elements, lower upper, define lower upper truncation distribution. Defaults list(lower = -Inf, upper = Inf). truncation automatically set bounds support. prior_weights prior odds associated given distribution. value passed model fitting function, creates models corresponding combinations prior distributions model parameters sets model priors odds product prior distributions.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a prior distribution — prior","text":"prior prior_none return object class 'prior'. named list containing distribution name, parameters, prior weights.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a prior distribution — prior","text":"","code":"# create a standard normal prior distribution p1 <- prior(distribution = \"normal\", parameters = list(mean = 1, sd = 1)) # create a half-normal standard normal prior distribution p2 <- prior(distribution = \"normal\", parameters = list(mean = 1, sd = 1), truncation = list(lower = 0, upper = Inf)) # the prior distribution can be visualized using the plot function # (see ?plot.prior for all options) plot(p1)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_factor.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a prior distribution for factors — prior_factor","title":"Creates a prior distribution for factors — prior_factor","text":"prior_factor creates prior distribution fitting models factor predictors. (Note results across different operating systems might vary due differences JAGS numerical precision.)","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_factor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a prior distribution for factors — prior_factor","text":"","code":"prior_factor( distribution, parameters, truncation = list(lower = -Inf, upper = Inf), prior_weights = 1, contrast = \"meandif\" )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_factor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a prior distribution for factors — prior_factor","text":"distribution name prior distribution. possible options \"point\" point density characterized location parameter. \"normal\" normal distribution characterized mean sd parameters. \"lognormal\" lognormal distribution characterized meanlog sdlog parameters. \"cauchy\" Cauchy distribution characterized location scale parameters. Internally converted generalized t-distribution df = 1. \"t\" generalized t-distribution characterized location, scale, df parameters. \"gamma\" gamma distribution characterized either shape rate, shape scale parameters. later internally converted shape rate parametrization \"invgamma\" inverse-gamma distribution characterized shape scale parameters. JAGS part uses 1/gamma distribution shape rate parameter. \"beta\" beta distribution characterized alpha beta parameters. \"exp\" exponential distribution characterized either rate scale parameter. later internally converted rate. \"uniform\" uniform distribution defined range b parameters list appropriate parameters given distribution. truncation list two elements, lower upper, define lower upper truncation distribution. Defaults list(lower = -Inf, upper = Inf). truncation automatically set bounds support. prior_weights prior odds associated given distribution. value passed model fitting function, creates models corresponding combinations prior distributions model parameters sets model priors odds product prior distributions. contrast type contrast prior distribution. possible options \"meandif\" contrast centered around grand mean equal marginal distributions, making prior distribution exchangeable across factor levels. contrast \"orthonormal\", marginal distributions identical regardless number factor levels specified prior distribution corresponds difference grand mean factor level. supports distribution = \"mnormal\" distribution = \"mt\" generates corresponding multivariate normal/t distributions. \"orthonormal\" contrast centered around grand mean equal marginal distributions, making prior distribution exchangeable across factor levels. supports distribution = \"mnormal\" distribution = \"mt\" generates corresponding multivariate normal/t distributions. \"treatment\" contrasts using first level comparison group setting equal prior distribution differences individual factor levels comparison level. \"independent\" contrasts specifying dependent prior distribution factor level (note leads overparameterized model intercept included).","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_factor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a prior distribution for factors — prior_factor","text":"return object class 'prior'.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_factor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a prior distribution for factors — prior_factor","text":"","code":"# create an orthonormal prior distribution p1 <- prior_factor(distribution = \"mnormal\", contrast = \"orthonormal\", parameters = list(mean = 0, sd = 1))"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_informed.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an informed prior distribution based on research — prior_informed","title":"Creates an informed prior distribution based on research — prior_informed","text":"prior_informed creates informed prior distribution based past research. prior can visualized plot function.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_informed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an informed prior distribution based on research — prior_informed","text":"","code":"prior_informed(name, parameter = NULL, type = \"smd\")"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_informed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an informed prior distribution based on research — prior_informed","text":"name name prior distribution. many options based prior psychological medical research. psychology, possible options \"van Erp\" informed prior distribution heterogeneity parameter tau meta-analytic effect size estimates based standardized mean differences (van Erp et al. 2017), \"Oosterwijk\" informed prior distribution effect sizes expected social psychology based prior elicitation dr. Oosterwijk (Gronau et al. 2017). medicine, possible options based Bartoš et al. (2021) Bartoš et al. (2023) developed empirical prior distributions effect size heterogeneity parameters continuous outcomes (standardized mean differences), dichotomous outcomes (logOR, logRR, risk differences), time event outcomes (logHR) based Cochrane database systematic reviews. Use \"Cochrane\" prior distribution based whole database call print(prior_informed_medicine_names) inspect names 46 subfields set appropriate parameter type. parameter parameter name describing prior distribution supposed produced cases name corresponds multiple prior distributions. Relevant empirical medical prior distributions. type prior type describing prior distribution supposed produced cases name parameter correspond multiple prior distributions. Relevant empirical medical prior distributions following options \"smd\" standardized mean differences \"logOR\" log odds ratios \"logRR\" log risk ratios \"RD\" risk differences \"logHR\" hazard ratios","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_informed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates an informed prior distribution based on research — prior_informed","text":"prior_informed returns object class 'prior'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_informed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates an informed prior distribution based on research — prior_informed","text":"details can found erp2017estimates;textualRoBMA, gronau2017bayesian;textualRoBMA, bartos2021bayesian;textualRoBMA.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_informed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates an informed prior distribution based on research — prior_informed","text":"","code":"# prior distribution representing expected effect sizes in social psychology # based on prior elicitation with dr. Oosterwijk p1 <- prior_informed(\"Oosterwijk\") # the prior distribution can be visualized using the plot function # (see ?plot.prior for all options) plot(p1) # empirical prior distribution for the standardized mean differences from the oral health # medical subfield based on meta-analytic effect size estimates from the # Cochrane database of systematic reviews p2 <- prior_informed(\"Oral Health\", parameter =\"effect\", type =\"smd\") print(p2) #> Student-t(0, 0.51, 5)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_none.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a prior distribution — prior_none","title":"Creates a prior distribution — prior_none","text":"prior creates prior distribution. prior can visualized plot function.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_none.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a prior distribution — prior_none","text":"","code":"prior_none(prior_weights = 1)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_none.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a prior distribution — prior_none","text":"prior_weights prior odds associated given distribution. value passed model fitting function, creates models corresponding combinations prior distributions model parameters sets model priors odds product prior distributions.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_none.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a prior distribution — prior_none","text":"prior prior_none return object class 'prior'. named list containing distribution name, parameters, prior weights.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_none.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a prior distribution — prior_none","text":"","code":"# create a standard normal prior distribution p1 <- prior(distribution = \"normal\", parameters = list(mean = 1, sd = 1)) # create a half-normal standard normal prior distribution p2 <- prior(distribution = \"normal\", parameters = list(mean = 1, sd = 1), truncation = list(lower = 0, upper = Inf)) # the prior distribution can be visualized using the plot function # (see ?plot.prior for all options) plot(p1)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PEESE.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a prior distribution for PET or PEESE models — prior_PEESE","title":"Creates a prior distribution for PET or PEESE models — prior_PEESE","text":"prior creates prior distribution fitting PET PEESE style models RoBMA. prior distribution can visualized plot function.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PEESE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a prior distribution for PET or PEESE models — prior_PEESE","text":"","code":"prior_PEESE( distribution, parameters, truncation = list(lower = 0, upper = Inf), prior_weights = 1 )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PEESE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a prior distribution for PET or PEESE models — prior_PEESE","text":"distribution name prior distribution. possible options \"point\" point density characterized location parameter. \"normal\" normal distribution characterized mean sd parameters. \"lognormal\" lognormal distribution characterized meanlog sdlog parameters. \"cauchy\" Cauchy distribution characterized location scale parameters. Internally converted generalized t-distribution df = 1. \"t\" generalized t-distribution characterized location, scale, df parameters. \"gamma\" gamma distribution characterized either shape rate, shape scale parameters. later internally converted shape rate parametrization \"invgamma\" inverse-gamma distribution characterized shape scale parameters. JAGS part uses 1/gamma distribution shape rate parameter. \"beta\" beta distribution characterized alpha beta parameters. \"exp\" exponential distribution characterized either rate scale parameter. later internally converted rate. \"uniform\" uniform distribution defined range b parameters list appropriate parameters given distribution. truncation list two elements, lower upper, define lower upper truncation distribution. Defaults list(lower = -Inf, upper = Inf). truncation automatically set bounds support. prior_weights prior odds associated given distribution. value passed model fitting function, creates models corresponding combinations prior distributions model parameters sets model priors odds product prior distributions.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PEESE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a prior distribution for PET or PEESE models — prior_PEESE","text":"prior_PET prior_PEESE return object class 'prior'.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PEESE.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a prior distribution for PET or PEESE models — prior_PEESE","text":"","code":"# create a half-Cauchy prior distribution # (PET and PEESE specific functions automatically set lower truncation at 0) p1 <- prior_PET(distribution = \"Cauchy\", parameters = list(location = 0, scale = 1)) plot(p1)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PET.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a prior distribution for PET or PEESE models — prior_PET","title":"Creates a prior distribution for PET or PEESE models — prior_PET","text":"prior creates prior distribution fitting PET PEESE style models RoBMA. prior distribution can visualized plot function.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a prior distribution for PET or PEESE models — prior_PET","text":"","code":"prior_PET( distribution, parameters, truncation = list(lower = 0, upper = Inf), prior_weights = 1 )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a prior distribution for PET or PEESE models — prior_PET","text":"distribution name prior distribution. possible options \"point\" point density characterized location parameter. \"normal\" normal distribution characterized mean sd parameters. \"lognormal\" lognormal distribution characterized meanlog sdlog parameters. \"cauchy\" Cauchy distribution characterized location scale parameters. Internally converted generalized t-distribution df = 1. \"t\" generalized t-distribution characterized location, scale, df parameters. \"gamma\" gamma distribution characterized either shape rate, shape scale parameters. later internally converted shape rate parametrization \"invgamma\" inverse-gamma distribution characterized shape scale parameters. JAGS part uses 1/gamma distribution shape rate parameter. \"beta\" beta distribution characterized alpha beta parameters. \"exp\" exponential distribution characterized either rate scale parameter. later internally converted rate. \"uniform\" uniform distribution defined range b parameters list appropriate parameters given distribution. truncation list two elements, lower upper, define lower upper truncation distribution. Defaults list(lower = -Inf, upper = Inf). truncation automatically set bounds support. prior_weights prior odds associated given distribution. value passed model fitting function, creates models corresponding combinations prior distributions model parameters sets model priors odds product prior distributions.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PET.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a prior distribution for PET or PEESE models — prior_PET","text":"prior_PET prior_PEESE return object class 'prior'.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_PET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a prior distribution for PET or PEESE models — prior_PET","text":"","code":"# create a half-Cauchy prior distribution # (PET and PEESE specific functions automatically set lower truncation at 0) p1 <- prior_PET(distribution = \"Cauchy\", parameters = list(location = 0, scale = 1)) plot(p1)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_weightfunction.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a prior distribution for a weight function — prior_weightfunction","title":"Creates a prior distribution for a weight function — prior_weightfunction","text":"prior_weightfunction creates prior distribution fitting RoBMA selection model. prior can visualized plot function.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_weightfunction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a prior distribution for a weight function — prior_weightfunction","text":"","code":"prior_weightfunction(distribution, parameters, prior_weights = 1)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_weightfunction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a prior distribution for a weight function — prior_weightfunction","text":"distribution name prior distribution. possible options \"two.sided\" two-sided weight function characterized vector steps vector alpha parameters. alpha parameter determines alpha parameter Dirichlet distribution cumulative sum used weights omega. \"one.sided\" one-sided weight function characterized either vector steps vector alpha parameter, leading monotonic one-sided function, vector steps, vector alpha1, vector alpha2 parameters leading non-monotonic one-sided weight function. alpha / alpha1 alpha2 parameters determine alpha parameter Dirichlet distribution cumulative sum used weights omega. parameters list appropriate parameters given distribution. prior_weights prior odds associated given distribution. model fitting function usually creates models corresponding combinations prior distributions model parameters, sets model priors odds product prior distributions.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_weightfunction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a prior distribution for a weight function — prior_weightfunction","text":"prior_weightfunction returns object class 'prior'.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/prior_weightfunction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a prior distribution for a weight function — prior_weightfunction","text":"","code":"p1 <- prior_weightfunction(\"one-sided\", parameters = list(steps = c(.05, .10), alpha = c(1, 1, 1))) # the prior distribution can be visualized using the plot function # (see ?plot.prior for all options) plot(p1)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA-package.html","id":null,"dir":"Reference","previous_headings":"","what":"RoBMA: Robust Bayesian meta-analysis — RoBMA-package","title":"RoBMA: Robust Bayesian meta-analysis — RoBMA-package","text":"RoBMA: Bayesian model-averaged meta-analysis adjustments publication bias ability specify informed prior distributions draw inference inclusion Bayes factors.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA-package.html","id":"user-guide","dir":"Reference","previous_headings":"","what":"User guide","title":"RoBMA: Robust Bayesian meta-analysis — RoBMA-package","text":"See bartos2021no;textualRoBMA, maier2020robust;textualRoBMA, bartos2020adjusting;textualRoBMA details regarding RoBMA methodology. details regarding customization model ensembles provided Reproducing BMA, BMA Medicine, Fitting Custom Meta-Analytic Ensembles vignettes. Please, use \"Issues\" section GitHub repository ask questions.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"RoBMA: Robust Bayesian meta-analysis — RoBMA-package","text":"František Bartoš f.bartos96@gmail.com","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a Robust Bayesian Meta-Analysis — RoBMA","title":"Estimate a Robust Bayesian Meta-Analysis — RoBMA","text":"RoBMA used estimate robust Bayesian meta-analysis. interface allows complete customization ensemble different prior (list prior) distributions component.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a Robust Bayesian Meta-Analysis — RoBMA","text":"","code":"RoBMA( d = NULL, r = NULL, logOR = NULL, OR = NULL, z = NULL, y = NULL, se = NULL, v = NULL, n = NULL, lCI = NULL, uCI = NULL, t = NULL, study_names = NULL, study_ids = NULL, data = NULL, weight = NULL, transformation = if (is.null(y)) \"fishers_z\" else \"none\", prior_scale = if (is.null(y)) \"cohens_d\" else \"none\", effect_direction = \"positive\", model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = list(prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_bias_null = prior_none(), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, chains = 3, sample = 5000, burnin = 2000, adapt = 500, thin = 1, parallel = FALSE, autofit = TRUE, autofit_control = set_autofit_control(), convergence_checks = set_convergence_checks(), save = \"all\", seed = NULL, silent = TRUE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a Robust Bayesian Meta-Analysis — RoBMA","text":"d vector effect sizes measured Cohen's d r vector effect sizes measured correlations logOR vector effect sizes measured log odds ratios vector effect sizes measured odds ratios z vector effect sizes measured Fisher's z y vector unspecified effect sizes (note effect size transformations unavailable type input) se vector standard errors effect sizes v vector variances effect sizes n vector overall sample sizes lCI vector lower bounds confidence intervals uCI vector upper bounds confidence intervals t vector t/z-statistics study_names optional argument names studies study_ids optional argument specifying dependency studies (using multilevel model). Defaults NULL studies independent. data data object created combine_data function. alternative input entry specifying d, r, y, etc... directly. .e., RoBMA function allow passing data.frame referencing columns. weight specifies likelihood weights individual estimates. Notes untested experimental feature. transformation transformation applied supplied effect sizes fitting individual models. Defaults \"fishers_z\". highly recommend using \"fishers_z\" transformation since variance stabilizing measure bias PET PEESE style models. options \"cohens_d\", correlation coefficient \"r\" \"logOR\". Supplying \"none\" treat effect sizes unstandardized refrain transformations. prior_scale effect size scale used define priors. Defaults \"cohens_d\". options \"fishers_z\", correlation coefficient \"r\", \"logOR\". prior scale need match effect sizes measure - samples prior distributions internally transformed match transformation data. prior_scale corresponds effect size scale default output, can changed within summary function. effect_direction expected direction effect. Correctly specifying expected direction effect crucial one-sided selection models, specify cut-offs using one-sided p-values. Defaults \"positive\" (another option \"negative\"). model_type string specifying RoBMA ensemble. Defaults NULL. options \"PSMA\", \"PP\", \"2w\" override settings passed priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, priors_effect. See details information different model types. priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults standard normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)). priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = .15)) based heterogeneities estimates psychology erp2017estimatesRoBMA. priors_bias list prior distributions publication bias adjustment component treated belonging alternative hypothesis. Defaults list( prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) ), corresponding RoBMA-PSMA model introduce bartos2021no;textualRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_bias_null list prior weight functions omega parameter treated belonging null hypothesis. Defaults publication bias adjustment, prior_none(). priors_hierarchical list prior distributions correlation random effects (rho) parameter treated belonging alternative hypothesis. setting allows users fit hierarchical (three-level) meta-analysis study_ids supplied. Note experimental feature see News details. Defaults beta distribution prior(distribution = \"beta\", parameters = list(alpha = 1, beta = 1)). priors_hierarchical_null list prior distributions correlation random effects (rho) parameter treated belonging null hypothesis. Defaults NULL. chains number chains MCMC algorithm. sample number sampling iterations MCMC algorithm. Defaults 5000. burnin number burnin iterations MCMC algorithm. Defaults 2000. adapt number adaptation iterations MCMC algorithm. Defaults 500. thin thinning chains MCMC algorithm. Defaults 1. parallel whether individual models fitted parallel. Defaults FALSE. implementation completely stable might cause connection error. autofit whether model fitted convergence criteria (specified autofit_control) satisfied. Defaults TRUE. autofit_control allows pass autofit control settings set_autofit_control() function. See ?set_autofit_control options default settings. convergence_checks automatic convergence checks assess fitted models, passed set_convergence_checks() function. See ?set_convergence_checks options default settings. save whether models posterior distributions kept obtaining model-averaged result. Defaults \"\" remove anything. Set \"min\" significantly reduce size final object, however, model diagnostics manipulation object possible. seed seed set model fitting, marginal likelihood computation, posterior mixing reproducibility results. Defaults NULL - seed set. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a Robust Bayesian Meta-Analysis — RoBMA","text":"RoBMA returns object class 'RoBMA'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a Robust Bayesian Meta-Analysis — RoBMA","text":"default settings RoBMA 2.0 package corresponds RoBMA-PSMA ensemble proposed bartos2021no;textualRoBMA. previous versions package (.e., RoBMA < 2.0) used specifications proposed maier2020robust;textualRoBMA (specification can easily obtained setting model_type = \"2w\". RoBMA-PP specification bartos2021no;textualRoBMA can obtained setting model_type = \"PP\". vignette(\"CustomEnsembles\", package = \"RoBMA\") vignette(\"ReproducingBMA\", package = \"RoBMA\") vignettes describe use RoBMA() fit custom meta-analytic ensembles (see prior(), prior_weightfunction(), prior_PET(), prior_PEESE() information prior distributions). RoBMA function first generates models combination provided priors model parameters. , individual models fitted using autorun.jags function. marginal likelihood computed using bridge_sampler function. individual models combined ensemble using posterior model probabilities using BayesTools package. Generic summary.RoBMA(), print.RoBMA(), plot.RoBMA() functions provided facilitate manipulation ensemble. visual check individual model diagnostics can obtained using diagnostics() function. fitted model can updated modified update.RoBMA() function.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a Robust Bayesian Meta-Analysis — RoBMA","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Bem 2011 and fitting the default (RoBMA-PSMA) model fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study) # in order to speed up the process, we can turn the parallelization on fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, parallel = TRUE) # we can get a quick overview of the model coefficients just by printing the model fit # a more detailed overview using the summary function (see '?summary.RoBMA' for all options) summary(fit) # the model-averaged effect size estimate can be visualized using the plot function # (see ?plot.RoBMA for all options) plot(fit, parameter = \"mu\") # forest plot can be obtained with the forest function (see ?forest for all options) forest(fit) # plot of the individual model estimates can be obtained with the plot_models function # (see ?plot_models for all options) plot_models(fit) # diagnostics for the individual parameters in individual models can be obtained using diagnostics # function (see 'diagnostics' for all options) diagnostics(fit, parameter = \"mu\", type = \"chains\") # the RoBMA-PP can be fitted with addition of the 'model_type' argument fit_PP <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, model_type = \"PP\") # as well as the original version of RoBMA (with two weightfunctions) fit_original <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, model_type = \"2w\") # or different prior distribution for the effect size (e.g., a half-normal distribution) # (see 'vignette(\"CustomEnsembles\")' for a detailed guide on specifying a custom model ensemble) fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study, priors_effect = prior(\"normal\", parameters = list(0, 1), truncation = list(0, Inf))) } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.reg.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg","title":"Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg","text":"RoBMA used estimate robust Bayesian meta-regression. interface allows complete customization ensemble different prior (list prior) distributions component.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.reg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg","text":"","code":"RoBMA.reg( formula, data, test_predictors = TRUE, study_names = NULL, study_ids = NULL, transformation = if (any(colnames(data) != \"y\")) \"fishers_z\" else \"none\", prior_scale = if (any(colnames(data) != \"y\")) \"cohens_d\" else \"none\", standardize_predictors = TRUE, effect_direction = \"positive\", priors = NULL, model_type = NULL, priors_effect = prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)), priors_heterogeneity = prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = 0.15)), priors_bias = list(prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)), priors_effect_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_heterogeneity_null = prior(distribution = \"point\", parameters = list(location = 0)), priors_bias_null = prior_none(), priors_hierarchical = prior(\"beta\", parameters = list(alpha = 1, beta = 1)), priors_hierarchical_null = NULL, prior_covariates = prior(\"normal\", parameters = list(mean = 0, sd = 0.25)), prior_covariates_null = prior(\"spike\", parameters = list(location = 0)), prior_factors = prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"), prior_factors_null = prior_factor(\"spike\", parameters = list(location = 0), contrast = \"meandif\"), chains = 3, sample = 5000, burnin = 2000, adapt = 500, thin = 1, parallel = FALSE, autofit = TRUE, autofit_control = set_autofit_control(), convergence_checks = set_convergence_checks(), save = \"all\", seed = NULL, silent = TRUE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.reg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg","text":"formula formula meta-regression model data data.frame containing data meta-regression. Note column names correspond effect sizes (d, logOR, , r, z), measure sampling variability (se, v, n, lCI, uCI, t), predictors. See combine_data() complete list reserved names additional information specifying input data. test_predictors vector predictor names test presence moderation (.e., assigned null alternative prior distributions). Defaults TRUE, predictors tested using default prior distributions (.e., prior_covariates, prior_covariates_null, prior_factors, prior_factors_null). estimate adjust effect predictors use FALSE. priors specified, settings test_predictors overridden. study_names optional argument names studies study_ids optional argument specifying dependency studies (using multilevel model). Defaults NULL studies independent. transformation transformation applied supplied effect sizes fitting individual models. Defaults \"fishers_z\". highly recommend using \"fishers_z\" transformation since variance stabilizing measure bias PET PEESE style models. options \"cohens_d\", correlation coefficient \"r\" \"logOR\". Supplying \"none\" treat effect sizes unstandardized refrain transformations. prior_scale effect size scale used define priors. Defaults \"cohens_d\". options \"fishers_z\", correlation coefficient \"r\", \"logOR\". prior scale need match effect sizes measure - samples prior distributions internally transformed match transformation data. prior_scale corresponds effect size scale default output, can changed within summary function. standardize_predictors whether continuous predictors standardized prior estimating model. Defaults TRUE. effect_direction expected direction effect. Correctly specifying expected direction effect crucial one-sided selection models, specify cut-offs using one-sided p-values. Defaults \"positive\" (another option \"negative\"). priors named list prior distributions predictor (names corresponding predictors). allows users specify null alternative hypothesis prior distributions predictor assigning corresponding element named list another named list (\"null\" \"alt\"). one prior specified given parameter, assumed correspond alternative hypotheses default null hypothesis specified (.e., prior_covariates_null prior_factors_null). named list one named prior distribution provided (either \"null\" \"alt\"), prior distribution used default distribution filled . Parameters without specified prior distributions assumed adjusted using default alternative hypothesis prior distributions (.e., prior_covariates prior_factors). priors specified, test_predictors ignored. model_type string specifying RoBMA ensemble. Defaults NULL. options \"PSMA\", \"PP\", \"2w\" override settings passed priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, priors_effect. See details information different model types. priors_effect list prior distributions effect size (mu) parameter treated belonging alternative hypothesis. Defaults standard normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 1)). priors_heterogeneity list prior distributions heterogeneity tau parameter treated belonging alternative hypothesis. Defaults prior(distribution = \"invgamma\", parameters = list(shape = 1, scale = .15)) based heterogeneities estimates psychology erp2017estimatesRoBMA. priors_bias list prior distributions publication bias adjustment component treated belonging alternative hypothesis. Defaults list( prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"two.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = \"one.sided\", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = \"Cauchy\", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = \"Cauchy\", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) ), corresponding RoBMA-PSMA model introduce bartos2021no;textualRoBMA. priors_effect_null list prior distributions effect size (mu) parameter treated belonging null hypothesis. Defaults point null hypotheses zero, prior(distribution = \"point\", parameters = list(location = 0)). priors_heterogeneity_null list prior distributions heterogeneity tau parameter treated belonging null hypothesis. Defaults point null hypotheses zero (fixed effect meta-analytic models), prior(distribution = \"point\", parameters = list(location = 0)). priors_bias_null list prior weight functions omega parameter treated belonging null hypothesis. Defaults publication bias adjustment, prior_none(). priors_hierarchical list prior distributions correlation random effects (rho) parameter treated belonging alternative hypothesis. setting allows users fit hierarchical (three-level) meta-analysis study_ids supplied. Note experimental feature see News details. Defaults beta distribution prior(distribution = \"beta\", parameters = list(alpha = 1, beta = 1)). priors_hierarchical_null list prior distributions correlation random effects (rho) parameter treated belonging null hypothesis. Defaults NULL. prior_covariates prior distributions regression parameter continuous covariates effect size alternative hypothesis (unless set explicitly priors). Defaults relatively wide normal distribution prior(distribution = \"normal\", parameters = list(mean = 0, sd = 0.25)). prior_covariates_null prior distributions regression parameter continuous covariates effect size null hypothesis (unless set explicitly priors). Defaults effect prior(\"spike\", parameters = list(location = 0)). prior_factors prior distributions regression parameter categorical covariates effect size alternative hypothesis (unless set explicitly priors). Defaults relatively wide multivariate normal distribution specifying differences mean contrasts prior_factor(\"mnormal\", parameters = list(mean = 0, sd = 0.25), contrast = \"meandif\"). prior_factors_null prior distributions regression parameter categorical covariates effect size null hypothesis (unless set explicitly priors). Defaults effect prior(\"spike\", parameters = list(location = 0)). chains number chains MCMC algorithm. sample number sampling iterations MCMC algorithm. Defaults 5000. burnin number burnin iterations MCMC algorithm. Defaults 2000. adapt number adaptation iterations MCMC algorithm. Defaults 500. thin thinning chains MCMC algorithm. Defaults 1. parallel whether individual models fitted parallel. Defaults FALSE. implementation completely stable might cause connection error. autofit whether model fitted convergence criteria (specified autofit_control) satisfied. Defaults TRUE. autofit_control allows pass autofit control settings set_autofit_control() function. See ?set_autofit_control options default settings. convergence_checks automatic convergence checks assess fitted models, passed set_convergence_checks() function. See ?set_convergence_checks options default settings. save whether models posterior distributions kept obtaining model-averaged result. Defaults \"\" remove anything. Set \"min\" significantly reduce size final object, however, model diagnostics manipulation object possible. seed seed set model fitting, marginal likelihood computation, posterior mixing reproducibility results. Defaults NULL - seed set. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.reg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg","text":"RoBMA.reg returns object class 'RoBMA.reg'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.reg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg","text":"vignette(\"/MetaRegression\", package = \"RoBMA\") vignette describes use RoBMA.reg() function fit Bayesian meta-regression ensembles. See bartos2023robust;textualRoBMA details methodology RoBMA() details function options. RoBMA.reg function first generates models combination provided priors model parameters. , individual models fitted using autorun.jags function. marginal likelihood computed using bridge_sampler function. individual models combined ensemble using posterior model probabilities using BayesTools package. Generic summary.RoBMA(), print.RoBMA(), plot.RoBMA() functions provided facilitate manipulation ensemble. visual check individual model diagnostics can obtained using diagnostics() function. fitted model can updated modified update.RoBMA() function. Estimated marginal means can computed marginal_summary() function visualized marginal_plot() function.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA.reg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a Robust Bayesian Meta-Analysis Meta-Regression — RoBMA.reg","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Andrews et al. (2021) and reproducing the example from # Bartos et al. (2024) with measure and age covariate. # note the the Andrews2021 data.frame columns identify the effect size \"r\" and # the standard error \"se\" of the effect size that are used to estimate the model fit_RoBMA <- RoBMA.reg(~ measure + age, data = Andrews2021, parallel = TRUE, seed = 1) # summarize the results summary(fit_RoBMA, output_scale = \"r\") # compute effect size estimates for each group marginal_summary(fit_RoBMA, output_scale = \"r\") # visualize the effect size estimates for each group marginal_plot(fit_RoBMA, parameter = \"measure\", output_scale = \"r\", lwd = 2) } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_control.html","id":null,"dir":"Reference","previous_headings":"","what":"Control MCMC fitting process — RoBMA_control","title":"Control MCMC fitting process — RoBMA_control","text":"Controls settings autofit process MCMC JAGS sampler (specifies termination criteria), values convergence checks.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_control.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control MCMC fitting process — RoBMA_control","text":"","code":"set_autofit_control( max_Rhat = 1.05, min_ESS = 500, max_error = NULL, max_SD_error = NULL, max_time = list(time = 60, unit = \"mins\"), sample_extend = 1000, restarts = 10 ) set_convergence_checks( max_Rhat = 1.05, min_ESS = 500, max_error = NULL, max_SD_error = NULL, remove_failed = FALSE, balance_probability = TRUE )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_control.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control MCMC fitting process — RoBMA_control","text":"max_Rhat maximum value R-hat diagnostic. Defaults 1.05. min_ESS minimum estimated sample size. Defaults 500. max_error maximum value MCMC error. Defaults NULL. aware PEESE publication bias adjustment can estimates different scale rest output, resulting relatively large max MCMC error. max_SD_error maximum value proportion MCMC error estimated SD parameter. Defaults NULL. max_time list time unit specifying maximum autofitting process per model. Passed difftime function (possible units \"secs\", \"mins\", \"hours\", \"days\", \"weeks\", \"years\"). Defaults list(time = 60, unit = \"mins\"). sample_extend number samples extend fitting process criteria satisfied. Defaults 1000. restarts number times new initial values generated case model fails initialize. Defaults 10. remove_failed whether models satisfying convergence checks removed inference. Defaults FALSE - warning raised. balance_probability whether prior model probability balanced across combinations models H0/H1 effect / heterogeneity / bias case non-convergence. Defaults TRUE.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_control.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control MCMC fitting process — RoBMA_control","text":"set_autofit_control returns list autofit control settings set_convergence_checks returns list convergence checks settings.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the RoBMA package — RoBMA_options","title":"Options for the RoBMA package — RoBMA_options","text":"placeholder object functions RoBMA package. (adapted runjags R package).","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Options for the RoBMA package — RoBMA_options","text":"","code":"RoBMA.options(...) RoBMA.get_option(name)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Options for the RoBMA package — RoBMA_options","text":"... named option(s) change - list available options, see details . name name option get current value - list available options, see details .","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/RoBMA_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Options for the RoBMA package — RoBMA_options","text":"current value available RoBMA options (applying changes specified) returned invisibly named list.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/sample_sizes.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample sizes to standard errors calculations — sample_sizes","title":"Sample sizes to standard errors calculations — sample_sizes","text":"Functions transforming standard errors sample sizes (assuming equal sample sizes per group).","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/sample_sizes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample sizes to standard errors calculations — sample_sizes","text":"","code":"se_d(d, n) n_d(d, se) se_r(r, n) n_r(r, se) se_z(n) n_z(se)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/sample_sizes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample sizes to standard errors calculations — sample_sizes","text":"d Cohen's d n sample size corresponding effect size se standard error corresponding effect size r correlation coefficient","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/sample_sizes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample sizes to standard errors calculations — sample_sizes","text":"Calculations Cohen's d, Fisher's z, log() based borenstein2011introductionRoBMA. Calculations correlation coefficient modified make standard error corresponding computed Fisher's z scale sample size (order make transformations consistent). case direct transformation available, transformations chained provide effect size interest. Note sample size standard error calculation log() available. standard error highly dependent odds within groups sample sizes individual events required. Theoretically, sample size obtained transforming effect size standard error different measure obtaining sample size using corresponding function, however, leads poor approximation recommended.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/standard_errors.html","id":null,"dir":"Reference","previous_headings":"","what":"Standard errors transformations — standard_errors","title":"Standard errors transformations — standard_errors","text":"Functions transforming standard errors different effect size measures.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/standard_errors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standard errors transformations — standard_errors","text":"","code":"se_d2se_logOR(se_d, logOR) se_d2se_r(se_d, d) se_r2se_d(se_r, r) se_logOR2se_d(se_logOR, logOR) se_d2se_z(se_d, d) se_r2se_z(se_r, r) se_r2se_logOR(se_r, r) se_logOR2se_r(se_logOR, logOR) se_logOR2se_z(se_logOR, logOR) se_z2se_d(se_z, z) se_z2se_r(se_z, z) se_z2se_logOR(se_z, z)"},{"path":"https://https://fbartos.github.io/RoBMA/reference/standard_errors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standard errors transformations — standard_errors","text":"se_d standard error Cohen's d logOR log(odds ratios) d Cohen's d se_r standard error correlation coefficient r correlation coefficient se_logOR standard error log(odds ratios) se_z standard error Fisher's z z Fisher's z","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/standard_errors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Standard errors transformations — standard_errors","text":"Transformations Cohen's d, Fisher's z, log() based borenstein2011introductionRoBMA. Calculations correlation coefficient modified make standard error corresponding computed Fisher's z scale sample size (order make transformations consistent). case direct transformation available, transformations chained provide effect size interest. important keep mind transformations approximations true values. experience, se_d2se_z works well values se(Cohen's d) < 0.5. forget effect sizes standardized variance Cohen's d = 1. Therefore, standard error study larger unless participants provided negative information (course, variance dependent effect size well, , can therefore larger). setting prior distributions, attempt transform standard normal distribution Cohen's d (mean = 0, sd = 1) normal distribution Fisher's z mean 0 sd = se_d2se_z(0, 1). approximation work well range values. Instead, approximate sd distribution Fisher's z using samples way: sd(d2z(rnorm(10000, 0, 1))) , specify distribution Cohen's d directly.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary.RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize fitted RoBMA object — summary.RoBMA","title":"Summarize fitted RoBMA object — summary.RoBMA","text":"summary.RoBMA creates summary tables RoBMA object.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary.RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize fitted RoBMA object — summary.RoBMA","text":"","code":"# S3 method for class 'RoBMA' summary( object, type = \"ensemble\", conditional = FALSE, output_scale = NULL, probs = c(0.025, 0.975), logBF = FALSE, BF01 = FALSE, short_name = FALSE, remove_spike_0 = FALSE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary.RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize fitted RoBMA object — summary.RoBMA","text":"object fitted RoBMA object type whether show overall RoBMA results (\"ensemble\"), overview individual models (\"models\"), overview individual models MCMC diagnostics (\"diagnostics\"), detailed summary individual models (\"individual\"). Can abbreviated first letters. conditional show conditional estimates (assuming alternative true). Defaults FALSE. available type == \"ensemble\". output_scale transform meta-analytic estimates different scale. Defaults NULL returns scale model estimated . probs quantiles posterior samples displayed. Defaults c(.025, .975) logBF show log Bayes factors. Defaults FALSE. BF01 show Bayes factors support null hypotheses. Defaults FALSE. short_name whether priors names shortened first (couple) letters. Defaults FALSE. remove_spike_0 whether spike prior distributions location zero omitted summary. Defaults FALSE. ... additional arguments","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary.RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize fitted RoBMA object — summary.RoBMA","text":"summary.RoBMA returns list tables class 'BayesTools_table'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary.RoBMA.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summarize fitted RoBMA object — summary.RoBMA","text":"See diagnostics() visual convergence checks individual models.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary.RoBMA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize fitted RoBMA object — summary.RoBMA","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Anderson et al. 2010 and fitting the default model # (note that the model can take a while to fit) fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n, study_names = Anderson2010$labels) # summary can provide many details about the model summary(fit) # estimates from the conditional models can be obtained with summary(fit, conditional = TRUE) # overview of the models and their prior and posterior probability, marginal likelihood, # and inclusion Bayes factor can be obtained with summary(fit, type = \"models\") # diagnostics overview, containing the maximum R-hat, minimum ESS, maximum MCMC error, and # maximum MCMC error / sd across parameters for each individual model can be obtained with summary(fit, type = \"diagnostics\") # summary of individual models and their parameters can be further obtained by summary(fit, type = \"individual\") } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary_heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarizes heterogeneity of a RoBMA model — summary_heterogeneity","title":"Summarizes heterogeneity of a RoBMA model — summary_heterogeneity","text":"Computes prediction interval, absolute heterogeneity (tau, tau^2), relative measures heterogeneity (^2, H^2) fitted RoBMA object.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary_heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarizes heterogeneity of a RoBMA model — summary_heterogeneity","text":"","code":"summary_heterogeneity( object, type = \"ensemble\", conditional = FALSE, output_scale = NULL, probs = c(0.025, 0.975), short_name = FALSE, remove_spike_0 = FALSE )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary_heterogeneity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarizes heterogeneity of a RoBMA model — summary_heterogeneity","text":"object fitted RoBMA object type whether show overall RoBMA results (\"ensemble\") detailed summary individual models (\"individual\"). Can abbreviated first letters. conditional show conditional estimates (assuming alternative true). Defaults FALSE. available type == \"ensemble\". output_scale transform meta-analytic estimates different scale. Defaults NULL returns scale model estimated . probs quantiles posterior samples displayed. Defaults c(.025, .975) short_name whether priors names shortened first (couple) letters. Defaults FALSE. remove_spike_0 whether spike prior distributions location zero omitted summary. Defaults FALSE.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/summary_heterogeneity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarizes heterogeneity of a RoBMA model — summary_heterogeneity","text":"summary.RoBMA returns list tables class 'BayesTools_table'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.BiBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Updates a fitted BiBMA object — update.BiBMA","title":"Updates a fitted BiBMA object — update.BiBMA","text":"update.BiBMA can used add additional model existing \"BiBMA\" object specifying either null alternative prior parameter prior odds model (prior_weights), see vignette(\"CustomEnsembles\") vignette, change prior odds fitted models specifying vector prior_weights length fitted models, refitting models failed converge updated settings control parameters, changing convergence criteria recalculating ensemble results specifying new control argument setting refit_failed == FALSE.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.BiBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updates a fitted BiBMA object — update.BiBMA","text":"","code":"# S3 method for class 'BiBMA' update( object, refit_failed = TRUE, extend_all = FALSE, prior_effect = NULL, prior_heterogeneity = NULL, prior_baseline = NULL, prior_weights = NULL, prior_effect_null = NULL, prior_heterogeneity_null = NULL, prior_baseline_null = NULL, study_names = NULL, chains = NULL, adapt = NULL, burnin = NULL, sample = NULL, thin = NULL, autofit = NULL, parallel = NULL, autofit_control = NULL, convergence_checks = NULL, save = \"all\", seed = NULL, silent = TRUE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.BiBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updates a fitted BiBMA object — update.BiBMA","text":"object fitted BiBMA object refit_failed whether failed models refitted. Relevant new priors prior_weights supplied. Defaults TRUE. extend_all extend sampling fitted models based \"sample_extend\" argument set_autofit_control() function. Defaults FALSE. prior_effect prior distribution effect size (mu) parameter treated belonging alternative hypothesis. Defaults NULL. prior_heterogeneity prior distribution heterogeneity tau parameter treated belonging alternative hypothesis. Defaults NULL. prior_baseline prior distribution intercepts (pi) study treated belonging alternative hypothesis. Defaults NULL. prior_weights either single value specifying prior model weight newly specified model using priors argument, vector length already fitted models update prior weights. prior_effect_null prior distribution effect size (mu) parameter treated belonging null hypothesis. Defaults NULL. prior_heterogeneity_null prior distribution heterogeneity tau parameter treated belonging null hypothesis. Defaults NULL. prior_baseline_null prior distribution intercepts (pi) study treated belonging null hypothesis. Defaults NULL. study_names optional argument names studies chains number chains MCMC algorithm. adapt number adaptation iterations MCMC algorithm. Defaults 500. burnin number burnin iterations MCMC algorithm. Defaults 2000. sample number sampling iterations MCMC algorithm. Defaults 5000. thin thinning chains MCMC algorithm. Defaults 1. autofit whether model fitted convergence criteria (specified autofit_control) satisfied. Defaults TRUE. parallel whether individual models fitted parallel. Defaults FALSE. implementation completely stable might cause connection error. autofit_control allows pass autofit control settings set_autofit_control() function. See ?set_autofit_control options default settings. convergence_checks automatic convergence checks assess fitted models, passed set_convergence_checks() function. See ?set_convergence_checks options default settings. save whether models posterior distributions kept obtaining model-averaged result. Defaults \"\" remove anything. Set \"min\" significantly reduce size final object, however, model diagnostics manipulation object possible. seed seed set model fitting, marginal likelihood computation, posterior mixing reproducibility results. Defaults NULL - seed set. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.BiBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updates a fitted BiBMA object — update.BiBMA","text":"BiBMA returns object class 'BiBMA'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.BiBMA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Updates a fitted BiBMA object — update.BiBMA","text":"See BiBMA() details.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.RoBMA.html","id":null,"dir":"Reference","previous_headings":"","what":"Updates a fitted RoBMA object — update.RoBMA","title":"Updates a fitted RoBMA object — update.RoBMA","text":"update.RoBMA can used add additional model existing \"RoBMA\" object specifying either null alternative prior parameter prior odds model (prior_weights), see vignette(\"CustomEnsembles\") vignette, change prior odds fitted models specifying vector prior_weights length fitted models, refitting models failed converge updated settings control parameters, changing convergence criteria recalculating ensemble results specifying new control argument setting refit_failed == FALSE.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.RoBMA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updates a fitted RoBMA object — update.RoBMA","text":"","code":"# S3 method for class 'RoBMA' update( object, refit_failed = TRUE, extend_all = FALSE, prior_effect = NULL, prior_heterogeneity = NULL, prior_bias = NULL, prior_hierarchical = NULL, prior_weights = NULL, prior_effect_null = NULL, prior_heterogeneity_null = NULL, prior_bias_null = NULL, prior_hierarchical_null = NULL, study_names = NULL, chains = NULL, adapt = NULL, burnin = NULL, sample = NULL, thin = NULL, autofit = NULL, parallel = NULL, autofit_control = NULL, convergence_checks = NULL, save = \"all\", seed = NULL, silent = TRUE, ... )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.RoBMA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updates a fitted RoBMA object — update.RoBMA","text":"object fitted RoBMA object refit_failed whether failed models refitted. Relevant new priors prior_weights supplied. Defaults TRUE. extend_all extend sampling fitted models based \"sample_extend\" argument set_autofit_control() function. Defaults FALSE. prior_effect prior distribution effect size (mu) parameter treated belonging alternative hypothesis. Defaults NULL. prior_heterogeneity prior distribution heterogeneity tau parameter treated belonging alternative hypothesis. Defaults NULL. prior_bias prior distribution publication bias adjustment component treated belonging alternative hypothesis. Defaults NULL. prior_hierarchical prior distribution correlation random effects (rho) parameter treated belonging alternative hypothesis. setting allows users fit hierarchical (three-level) meta-analysis study_ids supplied. Note experimental feature see News details. Defaults beta distribution prior(distribution = \"beta\", parameters = list(alpha = 1, beta = 1)). prior_weights either single value specifying prior model weight newly specified model using priors argument, vector length already fitted models update prior weights. prior_effect_null prior distribution effect size (mu) parameter treated belonging null hypothesis. Defaults NULL. prior_heterogeneity_null prior distribution heterogeneity tau parameter treated belonging null hypothesis. Defaults NULL. prior_bias_null prior distribution publication bias adjustment component treated belonging null hypothesis. Defaults NULL. prior_hierarchical_null prior distribution correlation random effects (rho) parameter treated belonging null hypothesis. Defaults NULL. study_names optional argument names studies chains number chains MCMC algorithm. adapt number adaptation iterations MCMC algorithm. Defaults 500. burnin number burnin iterations MCMC algorithm. Defaults 2000. sample number sampling iterations MCMC algorithm. Defaults 5000. thin thinning chains MCMC algorithm. Defaults 1. autofit whether model fitted convergence criteria (specified autofit_control) satisfied. Defaults TRUE. parallel whether individual models fitted parallel. Defaults FALSE. implementation completely stable might cause connection error. autofit_control allows pass autofit control settings set_autofit_control() function. See ?set_autofit_control options default settings. convergence_checks automatic convergence checks assess fitted models, passed set_convergence_checks() function. See ?set_convergence_checks options default settings. save whether models posterior distributions kept obtaining model-averaged result. Defaults \"\" remove anything. Set \"min\" significantly reduce size final object, however, model diagnostics manipulation object possible. seed seed set model fitting, marginal likelihood computation, posterior mixing reproducibility results. Defaults NULL - seed set. silent whether print messages regarding fitting process suppressed. Defaults TRUE. Note parallel = TRUE also suppresses messages. ... additional arguments.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.RoBMA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updates a fitted RoBMA object — update.RoBMA","text":"RoBMA returns object class 'RoBMA'.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.RoBMA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Updates a fitted RoBMA object — update.RoBMA","text":"See RoBMA() details.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/update.RoBMA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updates a fitted RoBMA object — update.RoBMA","text":"","code":"if (FALSE) { # \\dontrun{ # using the example data from Bem 2011 and fitting the default (RoBMA-PSMA) model fit <- RoBMA(d = Bem2011$d, se = Bem2011$se, study_names = Bem2011$study) # the update function allows us to change the prior model weights of each model fit1 <- update(fit, prior_weights = c(0, rep(1, 35))) # add an additional model with different priors specification # (see '?prior' for more information) fit2 <- update(fit, priors_effect_null = prior(\"point\", parameters = list(location = 0)), priors_heterogeneity = prior(\"normal\", parameters = list(mean = 0, sd = 1), truncation = list(lower = 0, upper = Inf)), priors_bias = prior_weightfunction(\"one-sided\", parameters = list(cuts = c(.05, .10, .20), alpha = c(1, 1, 1, 1)))) # update the models with an increased number of sample iterations fit3 <- update(fit, autofit_control = set_autofit_control(sample_extend = 1000), extend_all = TRUE) } # }"},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_multivariate_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted multivariate normal distribution — weighted_multivariate_normal","title":"Weighted multivariate normal distribution — weighted_multivariate_normal","text":"Density function weighted multivariate normal distribution mean, covariance matrix sigma, critical values crit_x, weights omega.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_multivariate_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted multivariate normal distribution — weighted_multivariate_normal","text":"x quantiles. p vector probabilities. mean mean sigma covariance matrix. crit_x vector critical values defining steps. omega vector weights defining probability observing t-statistics two steps. type type weight function (defaults \"two.sided\"). log, log.p logical; TRUE, probabilities p given log(p).","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_multivariate_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted multivariate normal distribution — weighted_multivariate_normal","text":".dwmnorm_fast returns density multivariate weighted normal distribution.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted normal distribution — weighted_normal","title":"Weighted normal distribution — weighted_normal","text":"Density, distribution function, quantile function random generation weighted normal distribution mean, standard deviation sd, steps steps (critical values) crit_x), weights omega.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted normal distribution — weighted_normal","text":"","code":"dwnorm( x, mean, sd, steps = if (!is.null(crit_x)) NULL, omega, crit_x = if (!is.null(steps)) NULL, type = \"two.sided\", log = FALSE ) pwnorm( q, mean, sd, steps = if (!is.null(crit_x)) NULL, omega, crit_x = if (!is.null(steps)) NULL, type = \"two.sided\", lower.tail = TRUE, log.p = FALSE ) qwnorm( p, mean, sd, steps = if (!is.null(crit_x)) NULL, omega, crit_x = if (!is.null(steps)) NULL, type = \"two.sided\", lower.tail = TRUE, log.p = FALSE ) rwnorm( n, mean, sd, steps = if (!is.null(crit_x)) NULL, omega, crit_x = if (!is.null(steps)) NULL, type = \"two.sided\" )"},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Weighted normal distribution — weighted_normal","text":"x, q vector quantiles. mean mean sd standard deviation. steps vector steps weight function. omega vector weights defining probability observing t-statistics two steps. crit_x vector critical values defining steps (steps supplied). type type weight function (defaults \"two.sided\"). log, log.p logical; TRUE, probabilities p given log(p). lower.tail logical; TRUE (default), probabilities \\(P[X \\le x]\\), otherwise, \\(P[X \\ge x]\\). p vector probabilities. n number observations. length(n) > 1, length taken number required.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Weighted normal distribution — weighted_normal","text":"dwnorm gives density, dwnorm gives distribution function, qwnorm gives quantile function, rwnorm generates random deviates.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/reference/weighted_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Weighted normal distribution — weighted_normal","text":"mean, sd, steps, omega can supplied vectors (mean, sd) matrices (steps, omega) length / number rows equal x/q/ p. Otherwise, recycled length result.","code":""},{"path":[]},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"features-3-2","dir":"Changelog","previous_headings":"","what":"Features","title":"version 3.2","text":"summary_heterogeneity() function summarize heterogeneity RoBMA models (prediction interval, tau, tau^2, ^2, H^2) check_RoBMA_convergence() function check convergence RoBMA models adds informed prior distributions binary time--event outcomes via BayesTools 0.2.17","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-3-2","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 3.2","text":"checking fixing number available cores upon loading package (hopefully fixes parallelization issues) update() function re-evaluates convergence checks individual models (https://github.com/FBartos/RoBMA/issues/34) typos minor issues vignettes","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"features-3-1","dir":"Changelog","previous_headings":"","what":"Features","title":"version 3.1","text":"binomial-normal models binary data via BiBMA function NoBMA NoBMA.reg() functions wrappers around RoBMA RoBMA.reg() functions simpler specification publication bias unadjusted Bayesian model-averaged meta-analysis adding odds ratios output transformation` extending (instead complete refitting) models via update.RoBMA() function (non-converged models default setting extend_all = TRUE)","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-3-1","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 3.1","text":"handling non-converged models","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-301","dir":"Changelog","previous_headings":"","what":"version 3.0.1","title":"version 3.0.1","text":"CRAN release: 2023-06-02","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-thanks-to-don--rens-3-0-1","dir":"Changelog","previous_headings":"","what":"Fixes (thanks to Don & Rens)","title":"version 3.0.1","text":"compilation issues Clang (https://github.com/FBartos/RoBMA/issues/28) lapack path specifications (https://github.com/FBartos/RoBMA/issues/24)","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"features-3-0","dir":"Changelog","previous_headings":"","what":"Features","title":"version 3.0","text":"meta-regression RoBMA.reg() function posterior marginal summary plots RoBMA.reg models summary_marginal() plot_marginal() functions new vignette hierarchical Bayesian model-averaged meta-analysis new vignette robust Bayesian model-averaged meta-regression adding vignette AMPPS tutorial faster implementation JAGS multivariate normal distribution (based BUGS JAGS module) incorporating weight argument RoBMA combine_data functions order pass custom likelihood weights ability use inverse square weights weighted meta-analysis setting weighted_type = \"inverse_sqrt\" argument","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"changes-3-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"version 3.0","text":"reworked interface hierarchical models. Prior distributions now specified via priors_hierarchical priors_hierarchical_null arguments instead priors_rho priors_rho_null. model summary now shows Hierarchical component summary.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-232","dir":"Changelog","previous_headings":"","what":"version 2.3.2","title":"version 2.3.2","text":"CRAN release: 2023-03-13","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-2-3-2","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 2.3.2","text":"suppressing start-message cleaning imports","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-231","dir":"Changelog","previous_headings":"","what":"version 2.3.1","title":"version 2.3.1","text":"CRAN release: 2022-07-16","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-2-3-1","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 2.3.1","text":"fixing weighted meta-analysis parameterization","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"features-2-3","dir":"Changelog","previous_headings":"","what":"Features","title":"version 2.3","text":"weighted meta-analysis specifying study_ids argument RoBMA() setting weighted = TRUE. likelihood contribution estimates study -weighted proportionally number estimates study. Note experimental feature supposed provide conservative alternative estimating RoBMA cases multiple estimates study multivariate option computationally feasible.","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-2-2-3","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 2.2.3","text":"updating Makevars install R 4.2 JAGS 4.3.1","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-222","dir":"Changelog","previous_headings":"","what":"version 2.2.2","title":"version 2.2.2","text":"CRAN release: 2022-04-20","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-2-2-2","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 2.2.2","text":"updating C++ compile M1 Mac","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-221","dir":"Changelog","previous_headings":"","what":"version 2.2.1","title":"version 2.2.1","text":"CRAN release: 2022-04-06","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"changes-2-2-1","dir":"Changelog","previous_headings":"","what":"Changes","title":"version 2.2.1","text":"message effect size scale parameter estimates always shown compatibility BayesTools 0.2.0+","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"features-2-2","dir":"Changelog","previous_headings":"","what":"Features","title":"version 2.2","text":"three-level meta-analysis specifying study_ids argument RoBMA. However, note (1) experimental feature (2) computational expense fitting selection models clustering extreme. now, almost impossible 2-3 estimates clustered within single study).","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-212","dir":"Changelog","previous_headings":"","what":"version 2.1.2","title":"version 2.1.2","text":"CRAN release: 2022-01-12","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-2-1-2","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 2.1.2","text":"adding Windows ucrt patch (thanks Tomas Kalibera) adding BayesTools version check","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-211","dir":"Changelog","previous_headings":"","what":"version 2.1.1","title":"version 2.1.1","text":"CRAN release: 2021-11-03","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-2-1-1","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 2.1.1","text":"incorrectly formatted citations vignettes capitalization","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"features-2-1-1","dir":"Changelog","previous_headings":"","what":"Features","title":"version 2.1.1","text":"adding informed_prior() function (BayesTools package) allows specification various informed prior distributions field medicine psychology adding vignette reproducing example dentine sensitivity informed Bayesian model-averaged meta-analysis Bartoš et al., 2021 (open-access), reductions fitted object size setting save = \"min\"","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-2-1","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 2.1","text":"informative error message JAGS module fails load correcting wrong PEESE transformation individual models summaries (issue #12) fixing error message missing conditional PET-PEESE fixing incorrect lower bound check log()","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"features-2-1","dir":"Changelog","previous_headings":"","what":"Features","title":"version 2.1","text":"adding interpret() function (issue #11) adding effect size transformation via output_scale argument plot() plot_models() functions better handling effect size transformations scaling - BayesTools style back-end functions Jacobian transformations","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-20","dir":"Changelog","previous_headings":"","what":"version 2.0","title":"version 2.0","text":"Please notice major release breaks backwards compatibility.","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"changes-2-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"version 2.0","text":"naming arguments specifying prior distributions different parameters/components models changed (priors_mu -> priors_effect, priors_tau -> priors_heterogeneity, priors_omega -> priors_bias), prior distributions specifying weight functions now use dedicated function (prior(distribution = \"two.sided\", parameters = ...) -> prior_weightfunction(distribution = \"two.sided\", parameters = ...)), new dedicated function specifying publication bias adjustment component / heterogeneity component (prior_none()), new dedicated functions specifying models PET PEESE publication bias adjustments (prior_PET(distribution = \"Cauchy\", parameters = ...) prior_PEESE(distribution = \"Cauchy\", parameters = ...)), new default prior distribution specification publication bias adjustment part models (corresponding RoBMA-PSMA model Bartoš et al., 2021 preprint), new model_type argument allowing specify different “pre-canned” models (\"PSMA\" = RoBMA-PSMA, \"PP\" = RoBMA-PP, \"2w\" = corresponding Maier et al., press , manuscript), combine_data function allows combination different effect sizes / variability measures common effect size measure (also used within RoBMA function), better improved automatic fitting procedure now enabled default (can turned autofit = FALSE) prior distributions can specified different scale supplied effect sizes (package fits model Fisher’s z scale back transforms results back scale used prior distributions specification, Cohen’s d default, can overwritten prior_scale transformation arguments), new prior distributions, e.g., beta fixed weight functions, estimates individual models now plotted plot_models() function forest plot can obtained forest() function, posterior distribution plots individual weights able supported, however, weightfunction PET-PEESE publication bias adjustments can visualized plot.RoBMA() function parameter = \"weightfunction\" parameter = \"PET-PEESE\".","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-121","dir":"Changelog","previous_headings":"","what":"version 1.2.1","title":"version 1.2.1","text":"CRAN release: 2021-02-16","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-1-2-1","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 1.2.1","text":"check_setup function working ","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-120","dir":"Changelog","previous_headings":"","what":"version 1.2.0","title":"version 1.2.0","text":"CRAN release: 2021-01-21","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"changes-1-2-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"version 1.2.0","text":"studies’s true effects now marginalized random effects models longer estimated (see Appendix prerint details). results, arguments referring true effects now disabled. models now estimated using likelihood effect sizes (instead test-statistics usually defined). reproduced simulation study used evaluate method performance achieved identical results (MCMC error, marginalizing true effects). big advantage using normal likelihood effect sizes considerable speed whole estimation process. results two changes, results models differ pre 1.2.0 version","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-1-2-0","dir":"Changelog","previous_headings":"","what":"Fixes","title":"version 1.2.0","text":"autofit turn control argument specified","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-112","dir":"Changelog","previous_headings":"","what":"version 1.1.2","title":"version 1.1.2","text":"CRAN release: 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specified ordering estimated observed effects requested simultaneously formatting file (NEWS.md)","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"improvements-1-0-5","dir":"Changelog","previous_headings":"","what":"Improvements:","title":"version 1.0.5","text":"priors plot: parameter specification, default plotting range, clearer x-axis labels cases parameter defined transformed scale parameters plots: probability scale always ends spot last tick density scale adding warnings specified models Rhat higher 1.05 specified value grouping warnings messages together","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"version-104","dir":"Changelog","previous_headings":"","what":"version 1.0.4","title":"version 1.0.4","text":"CRAN release: 2020-08-07","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-1-0-4","dir":"Changelog","previous_headings":"","what":"Fixes:","title":"version 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messages","code":""},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"cram-requested-changes-1-0-3","dir":"Changelog","previous_headings":"","what":"CRAM requested changes:","title":"version 1.0.3","text":"changing information messages ‘cat’ ‘message’ plot related functions saving returning ‘par’ settings user defined one base plot functions","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-1-0-2","dir":"Changelog","previous_headings":"","what":"Fixes:","title":"version 1.0.2","text":"summary plot function now shows quantile based confidence intervals individual models instead HPD provided (affects ‘summary’/‘plot’ ‘type = “individual”’, confidence intervals quantile based )","code":""},{"path":[]},{"path":"https://https://fbartos.github.io/RoBMA/news/index.html","id":"fixes-1-0-1","dir":"Changelog","previous_headings":"","what":"Fixes:","title":"version 1.0.1","text":"summary function returning median instead 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