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<b>brms</b> : An <i>R</i> Package for Bayesian Multilevel Models Using <i>Stan</i>

Paul‐Christian Bürkner · Journal of Statistical Software · 2017

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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.

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APA 7

Bürkner, P. (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. https://doi.org/10.18637/jss.v080.i01

MLA

Bürkner, Paul‐Christian. "brms: An R Package for Bayesian Multilevel Models Using Stan." 2017. https://doi.org/10.18637/jss.v080.i01.

Chicago

Bürkner, Paul‐Christian. 2017. "brms: An R Package for Bayesian Multilevel Models Using Stan.". https://doi.org/10.18637/jss.v080.i01.

Harvard

Bürkner, P. 2017, brms: An R Package for Bayesian Multilevel Models Using Stan, Journal of Statistical Software, available at: https://doi.org/10.18637/jss.v080.i01 [Accessed 7 Aug. 2026].

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Title
<b>brms</b> : An <i>R</i> Package for Bayesian Multilevel Models Using <i>Stan</i>
Author / contributors
Paul‐Christian Bürkner
Publisher
Journal of Statistical Software
Publication year
2017
Language
English

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