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.
Keywords
Akaike information criterionComputer scienceRange (aeronautics)Linear modelBayesian probabilityCovarianceMultilevel modelGeneralized linear modelPoisson distributionContext (archaeology)Negative binomial distributionStatisticsMathematicsArtificial intelligenceMachine learning
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Publication Info
- Year
- 2017
- Type
- article
- Volume
- 80
- Issue
- 1
- Citations
- 8224
- Access
- Closed
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Cite This
Paul‐Christian Bürkner
(2017).
<b>brms</b>: An <i>R</i> Package for Bayesian Multilevel Models Using <i>Stan</i>.
Journal of Statistical Software
, 80
(1)
.
https://doi.org/10.18637/jss.v080.i01
Identifiers
- DOI
- 10.18637/jss.v080.i01