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 both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, 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 prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Version:	2.22.0
Depends:	R (≥ 3.6.0), Rcpp (≥ 0.12.0), methods
Imports:	rstan (≥ 2.29.0), ggplot2 (≥ 2.0.0), loo (≥ 2.8.0), posterior (≥ 1.6.0), Matrix (≥ 1.1.1), mgcv (≥ 1.8-13), rstantools (≥ 2.1.1), bayesplot (≥ 1.5.0), bridgesampling (≥ 0.3-0), glue (≥ 1.3.0), rlang (≥ 1.0.0), future (≥ 1.19.0), future.apply (≥ 1.0.0), matrixStats, nleqslv, nlme, coda, abind, stats, utils, parallel, grDevices, backports
Suggests:	testthat (≥ 0.9.1), emmeans (≥ 1.4.2), cmdstanr (≥ 0.5.0), projpred (≥ 2.0.0), priorsense (≥ 1.0.0), shinystan (≥ 2.4.0), splines2 (≥ 0.5.0), RWiener, rtdists, extraDistr, processx, mice, spdep, mnormt, lme4, MCMCglmm, ape, arm, statmod, digest, diffobj, R.rsp, gtable, shiny, knitr, rmarkdown
Published:	2024-09-23
DOI:	10.32614/CRAN.package.brms
Author:	Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb], Hamada S. Badr [ctb], Frank Weber [ctb], Aki Vehtari [ctb], Mattan S. Ben-Shachar [ctb], Hayden Rabel [ctb], Simon C. Mills [ctb], Stephen Wild [ctb], Ven Popov [ctb]
Maintainer:	Paul-Christian Bürkner <paul.buerkner at gmail.com>
BugReports:	https://github.com/paul-buerkner/brms/issues
License:	GPL-2
URL:	https://github.com/paul-buerkner/brms, https://discourse.mc-stan.org/, https://paulbuerkner.com/brms/
NeedsCompilation:	no
Additional_repositories:	https://stan-dev.r-universe.dev/
Citation:	brms citation info
Materials:	README NEWS
In views:	Bayesian, MetaAnalysis, MixedModels, Phylogenetics
CRAN checks:	brms results

Documentation:

Reference manual:

brms.pdf

Vignettes:

Define Custom Response Distributions with brms (source, R code) Estimating Distributional Models with brms (source, R code) Parameterization of Response Distributions in brms (source) Handle Missing Values with brms (source, R code) Estimating Monotonic Effects with brms (source, R code) Estimating Multivariate Models with brms (source, R code) Estimating Non-Linear Models with brms (source, R code) Estimating Phylogenetic Multilevel Models with brms (source, R code) Running brms models with within-chain parallelization (source, R code) Multilevel Models with brms (source) Overview of the brms Package (source)

Downloads:

Package source:	brms_2.22.0.tar.gz
Windows binaries:	r-devel: brms_2.22.0.zip, r-release: brms_2.22.0.zip, r-oldrel: brms_2.22.0.zip
macOS binaries:	r-release (arm64): brms_2.22.0.tgz, r-oldrel (arm64): brms_2.22.0.tgz, r-release (x86_64): brms_2.22.0.tgz, r-oldrel (x86_64): brms_2.22.0.tgz
Old sources:	brms archive

Reverse dependencies:

Reverse depends:	bayesian, bayesnec, multimedia, neodistr, ordbetareg, pollimetry
Reverse imports:	BayesPostEst, bmm, bonsaiforest, brms.mmrm, brmsmargins, bsitar, chkptstanr, ESTER, exdqlm, flocker, IJSE, INSPECTumours, lehuynh, multilevelcoda, multilevelmediation, mvgam, PoolTestR, rmstBayespara, shinybrms, shrinkem, squid, webSDM
Reverse suggests:	afex, bayestestR, broom.helpers, broom.mixed, conformalbayes, datawizard, effectsize, emmeans, ggeffects, insight, loo, marginaleffects, modelbased, modelsummary, nlmixr2extra, novelforestSG, panelr, parameters, pcvr, performance, photosynthesis, priorsense, projpred, RBesT, report, see, sjPlot, sjstats, tidybayes, trending
Reverse enhances:	interactions, jtools, texreg

Linking:

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