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: 2.21.0
Depends: R (≥ 3.6.0), Rcpp (≥ 0.12.0), methods
Imports: rstan (≥ 2.29.0), ggplot2 (≥ 2.0.0), loo (≥ 2.3.1), posterior (≥ 1.0.0), 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), 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: 2024-03-20
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>
License: GPL-2
NeedsCompilation: no
Citation: brms citation info
Materials: README NEWS
In views: Bayesian, MetaAnalysis, MixedModels, Phylogenetics
CRAN checks: brms results


Reference manual: brms.pdf
Vignettes: Define Custom Response Distributions with brms
Estimating Distributional Models with brms
Parameterization of Response Distributions in brms
Handle Missing Values with brms
Estimating Monotonic Effects with brms
Estimating Multivariate Models with brms
Estimating Non-Linear Models with brms
Estimating Phylogenetic Multilevel Models with brms
Running brms models with within-chain parallelization
Multilevel Models with brms
Overview of the brms Package


Package source: brms_2.21.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): brms_2.21.0.tgz, r-oldrel (arm64): brms_2.21.0.tgz, r-release (x86_64): brms_2.21.0.tgz, r-oldrel (x86_64): brms_2.21.0.tgz
Old sources: brms archive

Reverse dependencies:

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


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