An introduction to Bayesian multilevel modeling with brms

The talk is about Bayesian multilevel models and their implementation in R using the package brms. It starts with a short introduction to multilevel modeling and to Bayesian statistics in general followed by an introduction to Stan, which is a flexible language for fitting open-ended Bayesian models. We then explain how to access Stan using the standard R formula syntax via the brms package. The package supports a wide range of response distributions and modeling options such as splines, autocorrelation, and censoring all in a multilevel context. A lot of post-processing and plotting methods are implemented as well. Some examples from Psychology and Medicine will be discussed.

Paul Bürkner is a statistician currently working as a 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 a 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 on optimal design and Bayesian data analysis. He has also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University (Finland).

Pual's website: https://paul-buerkner.github.io/about/
brms: https://github.com/paul-buerkner/brms

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