brms: Bayesian Regression Models using 'Stan'

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>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Version: 2.3.1
Depends: R (≥ 3.2.0), Rcpp (≥ 0.12.0), ggplot2 (≥ 2.0.0), methods
Imports: rstan (≥ 2.17.2), loo (≥ 2.0.0), Matrix (≥ 1.1.1), mgcv (≥ 1.8-13), rstantools (≥ 1.3.0), bayesplot (≥ 1.5.0), shinystan (≥ 2.4.0), bridgesampling (≥ 0.3-0), matrixStats, nleqslv, nlme, coda, abind, stats, utils, parallel, grDevices, backports
Suggests: testthat (≥ 0.9.1), RWiener, future, mice, spdep, mnormt, lme4, MCMCglmm, ape, arm, statmod, digest, R.rsp, knitr, rmarkdown
Published: 2018-06-05
Author: Paul-Christian Bürkner [aut, cre]
Maintainer: Paul-Christian Bürkner <paul.buerkner at gmail.com>
BugReports: https://github.com/paul-buerkner/brms/issues
License: GPL (≥ 3)
URL: https://github.com/paul-buerkner/brms, http://discourse.mc-stan.org
NeedsCompilation: no
Citation: brms citation info
Materials: README
In views: Bayesian
CRAN checks: brms results

Downloads:

Reference manual: brms.pdf
Vignettes: Define custom response distributions with brms
Fit Distributional Models with brms
Parameterization of response distributions in brms
Handle missing values with brms
Estimate monotonic effects with brms
Fit multivariate models with brms
Fit Non-Linear Models with brms
Fit phylogenetic models with brms
Multilevel Models with brms
Overview of the brms Package
Package source: brms_2.3.1.tar.gz
Windows binaries: r-devel: brms_2.3.1.zip, r-release: brms_2.3.1.zip, r-oldrel: brms_2.3.1.zip
OS X binaries: r-release: brms_2.3.1.tgz, r-oldrel: brms_2.3.1.tgz
Old sources: brms archive

Reverse dependencies:

Reverse imports: ESTER
Reverse suggests: broom, emmeans, jtools, sjPlot, sjstats

Linking:

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