brms (1.6.1)

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Bayesian Regression Models using Stan.

https://github.com/paul-buerkner/brms
https://groups.google.com/forum/#!forum/brms-users
http://cran.r-project.org/web/packages/brms

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 Buerkner
Author(s): Paul-Christian Brkner [aut, cre]

License: GPL (>= 3)

Uses: abind, bayesplot, coda, ggplot2, loo, Matrix, matrixStats, mgcv, nlme, Rcpp, rstan, rstantools, RWiener, shinystan, R.rsp, arm, mvtnorm, testthat, knitr, rmarkdown
Reverse suggests: broom

Released 14 days ago.


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