Advanced Bayesian Multilevel Modeling with the R Package brms
By Paul-Christian Burkner. The R Journal, 2017
2,401 citations recorded by the source corpus.
The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models, which are fitted with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted at the same time thus allowing for distributional regression. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines or Gaussian processes. To make all of these modeling options possible in a multilevel framework, brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of lme4. The purpose of the present paper is to introduce this syntax in detail and to demonstrate its usefulness with four examples, each showing other relevant aspects of the syntax.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/232a4da46a5f77ff1fc725fd06a4967a57c3b651
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In Artificial Intelligence
- Shravan Vasishth
- Daniela Mertzen
- Ina Bornkessel-Schlesewsky
- Andrew Gelman
- Martin Meyer
- Aitor Egurtzegi
- Lena A. Jäger
- Balthasar Bickel
In Statistics and Probability
- Daniela Mertzen
- Ina Bornkessel-Schlesewsky
- Andrew Gelman
- Martin Meyer
- Shravan Vasishth
- Tom Wenseleers
- Aitor Egurtzegi
- Lena A. Jäger
Citations in the corpus
- Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England (2021, methodology): “168) 67 (53-83) 120 (92-152) UK sequence separate-slopes binomial GLMM (1|LTLA/OBS) + REGION * DATE North West VOC vs.”
- The statistical significance filter leads to overoptimistic expectations of replicability (2018, background): “However, today, packages like brms (Bürkner, 2017, 2018) make Bayesian linear mixed models relatively easy to fit, and so it is now quite straightforward to compute Bayesian credible intervals.”
- Cross-linguistic differences in case marking shape neural power dynamics and gaze behavior during sentence planning. (2022, methodology): “Data were analyzed with Bayesian hierarchical regression on the single-trial level using the brms (version 2.13.0) interface to Stan (Bürkner, 2018; Bürkner, 2017; Carpenter et al., 2017) in R (R Core Team, 2021).”