The brms package (Bürkner, in press) implements Bayesian multilevel models in R using the probabilistic programming language Stan (Carpenter, 2017). A wide range of distributions and link functions are supported, allowing users to fit linear, robust linear, binomial, Poisson, survival, response times, ordinal, quantile, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include auto-correlation and smoothing terms, user defined dependence 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. In addition, model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.
