articleJournal of Statistical SoftwareJan 1, 2017DIAMOND OA

brms : An R Package for Bayesian Multilevel Models Using Stan

University of Münster

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Abstract

The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit - among others - linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with…

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Topics & keywords

Keywords
  • Akaike information criterion
  • Computer science
  • Range (aeronautics)
  • Linear model
  • Bayesian probability
  • Covariance
  • Multilevel model
  • Generalized linear model
UN Sustainable Development Goals
  • Peace, Justice and strong institutions
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