Bayesian Regression Models using Stan

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.


The brms package provides an interface to fit Bayesian generalized (non-)linear mixed models using Stan, which is a C++ package for obtaining Bayesian inference using the No-U-turn sampler (see The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses.


Reference manual

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