paul-buerkner/brms

Health v2 · maintenance only

Adoption not part of the score

1424 stars · 226 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

brms is an R package providing a formula-based interface (similar to lme4) for fitting Bayesian generalized (non-)linear multivariate multilevel models using Stan. It supports a wide range of response distributions, distributional regression, priors, and model comparison via posterior predictive checks, cross-validation, and Bayes factors.

Use cases

• fit bayesian multilevel regression models in r
• run hierarchical linear models with stan backend
• model count data with poisson regression and random effects
• compare bayesian models with loo and bayes factors
• fit ordinal or zero-inflated regression models
• do distributional regression with priors on all parameters

When to choose

• you want lme4-like formula syntax for bayesian multilevel modeling
• you need flexible response distributions, mixture models, or smooth/non-linear terms
• you want explicit prior specification and posterior predictive checks

When to avoid

• you need fast frequentist-only model fitting without MCMC overhead
• you work outside R or cannot install the Stan/C++ toolchain
• you need a simple GUI-based statistics tool

Facets

library · maturity stable

machine-learning data-science math data-science cross-platform bayesian-inference multilevel-models stan regression hierarchical-models mcmc r-package statistics r

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