Journal of Statistical Software· 2017Q1
<b>brms</b> : An <i>R</i> Package for Bayesian Multilevel Models Using <i>Stan</i>
- 9,492citations
- Q1SCImago
- 2017year
Short summary
The brms package in R provides a flexible framework for fitting complex Bayesian multilevel models using the Stan probabilistic programming language.
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Key points
- Implements Bayesian multilevel models in R via the Stan probabilistic programming language.
- Supports a broad range of distributions and link functions for diverse model types.
- Includes advanced modeling options such as autocorrelation, custom covariance, and censored data.
- Facilitates flexible prior specification and provides tools for model fit assessment (WAIC, LOO-CV).
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Journal of Statistical Software, 2017 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics