On Thursday, February 25th at 7 pm UTC | 8 pm CET, as part of the Why R? Webinar series, we have the honour to host Paul Bürkner, Junior Research Group Leader at the Cluster of Excellence SimTech at the University of Stuttgart (Germany). Author of the R package brms and member of the Stan Development Team, Paul will explain how the R package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, a C++ package for performing full Bayesian inference.
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- Paul Bürkner
Paul is a statistician currently working as an Independent Junior Research Group Leader at the Cluster of Excellence SimTech at the University of Stuttgart (Germany). He is the author of the R package brms and member of the Stan Development Team. Previously, he studied Psychology and Mathematics at the Universities of Münster and Hagen (Germany) and did his PhD in Münster about optimal design and Bayesian data analysis. Paul has also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University (Finland).
brms: Bayesian Regression Models using Stan
The brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, a C++ package for performing full Bayesian inference. The formula syntax is very similar to that of the lme4 package to provide a familiar and simple interface for performing regression analyses. A wide range of response distributions are supported, allowing users to fit – among others – linear, robust linear, count data, survival, response times, ordinal, zero-inflated, 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, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models, i.e., models with multiple response variables, can be fit as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that reflect their beliefs. Model fits can easily be assessed and compared with posterior predictive checks, cross-validation, and Bayes factors.