Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Paul-Christian Bürkner
Title: brms: An R Package for Bayesian Multilevel Models Using Stan
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 the Watanabe-Akaike information criterion and leave-one-out cross-validation.

Page views:: 27065. Submitted: 2015-09-24. Published: 2017-08-29.
Paper: brms: An R Package for Bayesian Multilevel Models Using Stan     Download PDF (Downloads: 15140)
brms_1.9.0.tar.gz: R source package Download (Downloads: 554; 4MB)
v80i01.R: R replication code Download (Downloads: 575; 2KB)
v80i01-efficiency.R: R replication code Download (Downloads: 455; 26KB)

DOI: 10.18637/jss.v080.i01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.