The R Journal: accepted article

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Advanced Bayesian Multilevel Modeling with the R Package brms PDF download
Paul-Christian Bürkner

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. Multivariate models, in which each response variable can be predicted using the above mentioned options, can be fitted as well. 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.

Received: 2017-10-01; online 2018-05-18
CRAN packages: brms, lme4, rstanarm, MCMCglmm, mgcv, nlme, afex, loo, gamlss.data, bridgesampling
CRAN Task Views implied by cited CRAN packages: Bayesian, SocialSciences, Econometrics, Environmetrics, Psychometrics, OfficialStatistics, SpatioTemporal, ChemPhys, Finance, Phylogenetics, Spatial, Survival


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-017,
  author = {Paul-Christian Bürkner},
  title = {{Advanced Bayesian Multilevel Modeling with the R Package
          brms}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-017/index.html}
}