DESCRIPTION

Package: brms
Encoding: UTF-8
Type: Package
Title: Bayesian Regression Models using 'Stan'
Version: 2.14.11
Date: 2021-02-06
Authors@R: 
    c(person("Paul-Christian", "Bürkner", email = "paul.buerkner@gmail.com",
             role = c("aut", "cre")),
      person("Jonah", "Gabry", role = c("ctb")),
      person("Sebastian", "Weber", role = c("ctb")),
      person("Andrew", "Johnson", role = c("ctb")),
      person("Martin", "Modrak", role = c("ctb")))
Depends: 
    R (>= 3.5.0),
    Rcpp (>= 0.12.0),
    methods
Imports: 
    rstan (>= 2.19.2),
    ggplot2 (>= 2.0.0),
    loo (>= 2.3.1),
    Matrix (>= 1.1.1),
    mgcv (>= 1.8-13),
    rstantools (>= 2.1.1),
    bayesplot (>= 1.5.0),
    shinystan (>= 2.4.0),
    projpred (>= 2.0.0),
    bridgesampling (>= 0.3-0),
    glue (>= 1.3.0),
    future (>= 1.19.0),
    matrixStats,
    nleqslv,
    nlme,
    coda,
    abind,
    stats,
    utils,
    parallel,
    grDevices,
    backports
Suggests: 
    testthat (>= 0.9.1),
    emmeans (>= 1.4.2),
    cmdstanr (>= 0.1.3),
    RWiener,
    rtdists,
    mice,
    spdep,
    mnormt,
    lme4,
    MCMCglmm,
    splines2,
    ape,
    arm,
    statmod,
    digest,
    diffobj,
    R.rsp,
    knitr,
    rmarkdown
Description: Fit Bayesian generalized (non-)linear multivariate multilevel models
    using 'Stan' for full Bayesian inference. A wide range of distributions 
    and link functions are supported, allowing users to fit -- among others -- 
    linear, robust linear, count data, survival, response times, ordinal, 
    zero-inflated, hurdle, and even self-defined mixture models all in a 
    multilevel context. Further modeling options include non-linear and 
    smooth terms, auto-correlation structures, censored data, meta-analytic 
    standard errors, and quite a few more. In addition, all parameters of the 
    response distribution can be predicted in order to perform distributional 
    regression. Prior specifications are flexible and explicitly encourage 
    users to apply prior distributions that actually reflect their beliefs.
    Model fit can easily be assessed and compared with posterior predictive 
    checks and leave-one-out cross-validation. References: Bürkner (2017)
    <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>;
    Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
LazyData: true
NeedsCompilation: no
License: GPL-2
URL: https://github.com/paul-buerkner/brms, https://discourse.mc-stan.org/
BugReports: https://github.com/paul-buerkner/brms/issues
Additional_repositories:
    https://mc-stan.org/r-packages/
VignetteBuilder: 
    knitr,
    R.rsp
RoxygenNote: 7.1.1