文章基本信息

标题：brms: An R Package for Bayesian Multilevel Models Using Stan
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作者：Paul-Christian Bürkner
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2017
卷号：80
期号：1
页码：1-28
DOI：10.18637/jss.v080.i01
语种：English
出版社：University of California, Los Angeles
摘要：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.
关键词：Bayesian inference;multilevel model;ordinal data;MCMC;Stan;R