Merge pull request #235 from easystats/dev

CRAN 0.3.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: bayestestR
 Type: Package
 Title: Understand and Describe Bayesian Models and Posterior Distributions
-Version: 0.2.5.9000
+Version: 0.3.0
 Authors@R: c(
     person("Dominique", 
 		"Makowski", 

R/ci.R

@@ -39,7 +39,7 @@
 #' ci(df, method = "HDI", ci = c(.80, .89, .95))
 #'
 #' library(rstanarm)
-#' model <- stan_glm(mpg ~ wt, data = mtcars, chains = 2, iter = 200)
+#' model <- stan_glm(mpg ~ wt, data = mtcars, chains = 2, iter = 200, refresh = 0)
 #' ci(model, method = "ETI", ci = c(.80, .89))
 #' ci(model, method = "HDI", ci = c(.80, .89))
 #' \dontrun{

R/hdi.R

@@ -65,7 +65,7 @@
 #' hdi(df, ci = c(.80, .90, .95))
 #'
 #' library(rstanarm)
-#' model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
+#' model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
 #' hdi(model)
 #' hdi(model, ci = c(.80, .90, .95))
 #'

R/mcse.R

@@ -17,7 +17,7 @@
 #' library(bayestestR)
 #' library(rstanarm)
 #'
-#' model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1)
+#' model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
 #' mcse(model)
 #' @importFrom insight get_parameters
 #' @export

R/simulate_prior.R

@@ -9,7 +9,7 @@
 #' library(bayestestR)
 #' library(rstanarm)
 #'
-#' model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1)
+#' model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
 #' simulate_prior(model)
 #' @export
 simulate_prior <- function(model, n = 1000, ...) {

README.Rmd

@@ -42,8 +42,8 @@ Existing R packages allow users to easily fit a large variety of models and extr
 
 You can reference the package and its documentation as follows:
 
-- Makowski, Ben-Shachar, \& Lüdecke (2019). *bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework*. Journal of Open Source Software, 4(40), 1541, https://doi.org/10.21105/joss.01541
-
+- Makowski, D., Ben-Shachar, M. S., \& Lüdecke, D. (2019). *bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework*. Journal of Open Source Software, 4(40), 1541. https://doi.org/10.21105/joss.01541
+- Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., \& Lüdecke, D. (2019). *Indices of Effect Existence and Significance in the Bayesian Framework*. *Under review*. https://doi.org/10.31234/osf.io/2zexr
 
 ## Installation
 

README.md

@@ -18,24 +18,23 @@ functions to analyze and describe posterior distributions generated by a
 variety of models objects, including popular modeling packages such as
 **rstanarm**, **brms** or **BayesFactor**.
 
-You can reference our work as follows:
+You can reference the package and its documentation as follows:
 
-  - Makowski, Ben-Shachar, & Lüdecke (2019). *bayestestR: Describing
-    Effects and their Uncertainty, Existence and Significance within the
-    Bayesian Framework*. Journal of Open Source Software, 4(40), 1541,
-    <https://doi.org/10.21105/joss.01541>
+  - Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2019). *bayestestR:
+    Describing Effects and their Uncertainty, Existence and Significance
+    within the Bayesian Framework*. Journal of Open Source Software,
+    4(40), 1541. <https://doi.org/10.21105/joss.01541>
+  - Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., & Lüdecke, D.
+    (2019). *Indices of Effect Existence and Significance in the
+    Bayesian Framework*. *Under review*.
+    <https://doi.org/10.31234/osf.io/2zexr>
 
 ## Installation
 
 Run the following:
 
 ``` r
-install.packages("devtools")
-devtools::install_github("easystats/bayestestR")
-```
-
-``` r
-library("bayestestR")
+install.packages(bayestestR)
 ```
 
 ## Documentation
@@ -92,9 +91,9 @@ cited below at once.
 ``` r
 describe_posterior(rnorm(1000))
 ##   Parameter Median CI CI_low CI_high   pd ROPE_CI ROPE_low ROPE_high
-## 1 Posterior -0.018 89   -1.6     1.7 0.51      89     -0.1       0.1
+## 1 Posterior  0.039 89   -1.8     1.6 0.52      89     -0.1       0.1
 ##   ROPE_Percentage
-## 1           0.086
+## 1             0.1
 ```
 
 ## Point-estimates
@@ -113,7 +112,7 @@ map_estimate(posterior)
 ## MAP = 0.40
 ```
 
-![](man/figures/unnamed-chunk-6-1.png)<!-- -->
+![](man/figures/unnamed-chunk-5-1.png)<!-- -->
 
 ## Uncertainty
 
@@ -156,7 +155,7 @@ eti(posterior, ci = .89)
 ##  [0.42, 7.27]
 ```
 
-![](man/figures/unnamed-chunk-8-1.png)<!-- -->
+![](man/figures/unnamed-chunk-7-1.png)<!-- -->
 
 ## Null-Hypothesis Significance Testing (NHST)
 
@@ -197,7 +196,7 @@ rope(posterior, range = c(-0.1, 0.1))
 ##       1.11 %
 ```
 
-![](man/figures/unnamed-chunk-10-1.png)<!-- -->
+![](man/figures/unnamed-chunk-9-1.png)<!-- -->
 
 ### Equivalence test
 
@@ -244,12 +243,10 @@ guidelines*](https://easystats.github.io/bayestestR/articles/guidelines.html).
 ``` r
 posterior <- distribution_normal(100, 0.4, 0.2)
 p_direction(posterior)
-## # Probability of Direction (pd)
-## 
 ## pd = 98.00%
 ```
 
-![](man/figures/unnamed-chunk-13-1.png)<!-- -->
+![](man/figures/unnamed-chunk-12-1.png)<!-- -->
 
 ### Bayes Factor
 
@@ -277,12 +274,12 @@ bayesfactor_parameters(posterior, prior, direction = "two-sided", null = 0)
 ## # Bayes Factor (Savage-Dickey density ratio)
 ## 
 ##  Bayes Factor
-##             2
+##          2.08
 ## 
 ## * Evidence Against The Null: [0]
 ```
 
-![](man/figures/unnamed-chunk-15-1.png)<!-- -->
+![](man/figures/unnamed-chunk-14-1.png)<!-- -->
 
 <sup>*The lollipops represent the density of a point-null on the prior
 distribution (the blue lollipop on the dotted distribution) and on the
@@ -306,12 +303,10 @@ divided by the density at the Maximum A Posteriori (MAP).
 ``` r
 posterior <- distribution_normal(100, 0.4, 0.2)
 p_map(posterior)
-## # MAP-based p-value
-## 
 ## p (MAP) = 0.193
 ```
 
-![](man/figures/unnamed-chunk-17-1.png)<!-- -->
+![](man/figures/unnamed-chunk-16-1.png)<!-- -->
 
 ## Utilities