Fit Bayesian generalized (non-)linear 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.

Maintainer: Paul-Christian Bürkner <paul.buerkner at gmail.com>

Author(s): Paul-Christian Bürkner*

Install package and any missing dependencies by running this line in your R console:

install.packages("brms")

Depends R (>= 3.2.0), Rcpp(>=0.12.0), ggplot2(>=2.0.0), methods
Imports rstan(>=2.14.2), loo(>=1.1.0), Matrix(>=1.1.1), mgcv(>=1.8-13), rstantools(>=1.3.0), bayesplot(>=1.3.0), shinystan(>=2.4.0), matrixStats, bridgesampling, nlme, coda, abind, stats, utils, parallel, grDevices,
Suggests testthat(>=0.9.1), RWiener, future, arm, spdep, mnormt, MCMCglmm, ape, R.rsp, knitr, rmarkdown
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Package brms
Materials
URL https://github.com/paul-buerkner/brms https://groups.google.com/forum/#!forum/brms-users
Task Views
Version 1.10.2
Published 2017-10-20
License GPL (>= 3)
BugReports https://github.com/paul-buerkner/brms/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks brms check results
Package source brms_1.10.2.tar.gz