brms: Bayesian Regression Models using Stan
Version 1.10.0

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.

Package details

AuthorPaul-Christian Bürkner [aut, cre]
Date of publication2017-09-09 21:51:28 UTC
MaintainerPaul-Christian Bürkner <[email protected]>
LicenseGPL (>= 3)
Version1.10.0
URL https://github.com/paul-buerkner/brms https://groups.google.com/forum/#!forum/brms-users
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("brms")

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brms documentation built on Sept. 10, 2017, 1:02 a.m.