“Advanced Bayesian Multilevel Modeling with the R Package brms” (2018), by Paul‐Christian Bürkner. Mathematics research paper. Original abstract below, unchanged. A free PDF is available.

Advanced Bayesian Multilevel Modeling with the R Package brms

Paul‐Christian Bürkner · January 1, 2018 · Mathematics

Abstract

The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models, which are fitted with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted at the same time thus allowing for distributional regression. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines or Gaussian processes. To make all of these modeling options possible in a multilevel framework, brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of lme4. The purpose of the present paper is to introduce this syntax in detail and to demonstrate its usefulness with four examples, each showing other relevant aspects of the syntax.

Free PDF DOI: 10.32614/rj-2018-017