brms : An R Package for Bayesian Multilevel Models Using Stan

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, an...

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Bibliographic Details
Published inJournal of statistical software Vol. 80; no. 1; pp. 1 - 28
Main Author Bürkner, Paul-Christian
Format Journal Article
LanguageEnglish
Published Foundation for Open Access Statistics 2017
Subjects
Bayesian inference
MCMC
multilevel model
ordinal data
Stan
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Summary: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 the Watanabe-Akaike information criterion and leave-one-out cross-validation.
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v080.i01