R packages interfacing with Stan: brms

January 11, 2017

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Over on the Stan users mailing list I (Jonah) recently posted about our new document providing guidelines for developing R packages interfacing with Stan. As I say in the post and guidelines, we (the Stan team) are excited to see the emergence of some very cool packages developed by our users. One of these packages is Paul Bürkner’s brms. Paul is currently working on his PhD in statistics at the University of Münster, having previously studied psychology and mathematics at the universities of Münster and Hagen (Germany). Here is Paul writing about brms:

The R package brms implements a wide variety of Bayesian regression models using extended lme4 formula syntax and Stan for the model fitting. It has been on CRAN for about one and a half years now and has grown to be probably one of the most flexible R packages when it comes to regression models.

A wide range of distributions are supported, allowing users to fit — among others — linear, robust linear, count data, response time, survival, ordinal, and zero-inflated models. You can incorporate multilevel structures, smooth terms, autocorrelation, as well as measurement error in predictor variables to mention only a few key features. Furthermore, non-linear predictor terms can be specified similar to how it is done in the nlme package and on top of that all parameters of the response distribution can be predicted at the same time.

After model fitting, you have many post-processing and plotting methods to choose from. For instance, you can investigate and compare model fit using leave-one-out cross-validation and posterior predictive checks or predict responses for new data.

If you are interested and want to learn more about brms, please use the following links:

  • GitHub repository (for source code, bug reports, feature requests)
  • CRAN website (for vignettes with guidance on how to use the package)
  • Wayne Folta’s blog posts (for interesting brms examples)

Also, a paper about brms will be published soon in the Journal of Statistical Software.

My thanks goes to the Stan Development Team for creating Stan, which is probably the most powerful and flexible tool for performing Bayesian inference, and for allowing me to introduce brms here at this blog.

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