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>.
| Version: | 2.13.5 |
| 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), bridgesampling (≥ 0.3-0), glue (≥ 1.3.0), matrixStats, nleqslv, nlme, coda, abind, future, stats, utils, parallel, grDevices, backports |
| Suggests: | testthat (≥ 0.9.1), emmeans (≥ 1.4.2), cmdstanr (≥ 0.0.0.9008), RWiener, rtdists, mice, spdep, mnormt, lme4, MCMCglmm, splines2, ape, arm, statmod, digest, R.rsp, knitr, rmarkdown |
| Published: | 2020-07-31 |
| Author: | Paul-Christian Bürkner [aut, cre], Jonah Gabry [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, http://discourse.mc-stan.org |
| NeedsCompilation: | no |
| Citation: | brms citation info |
| Materials: | README NEWS |
| In views: | Bayesian, 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 Multilevel Models with brms Overview of the brms Package |
| Package source: | brms_2.13.5.tar.gz |
| Windows binaries: | r-devel: brms_2.13.5.zip, r-release: brms_2.13.5.zip, r-oldrel: brms_2.13.5.zip |
| macOS binaries: | r-release: brms_2.13.5.tgz, r-oldrel: brms_2.13.5.tgz |
| Old sources: | brms archive |
| Reverse depends: | pollimetry |
| Reverse imports: | ESTER, shinybrms |
| Reverse suggests: | afex, bayestestR, broom.mixed, concurve, effectsize, emmeans, ggeffects, insight, loo, modelbased, panelr, parameters, performance, projpred, see, sjPlot, sjstats, tidybayes |
| Reverse enhances: | interactions, jtools, texreg |
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