# paul-buerkner/brms

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

Repository: https://github.com/paul-buerkner/brms
Canonical: https://ross.abutalabs.com/products/brms
Homepage: https://paulbuerkner.com/brms/
Language: R
License: GPL-2.0
License Family: copyleft
Topics: brms, stan, bayesian-inference, multilevel-models, statistical-models, r-package
Last push: 2026-08-24T10:50:39+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 8, longevity 100
- inputs: {"age_days": 4095, "days_push": 9, "days_rel": 701, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1424, forks 226 (observed 2026-08-28T04:04:41.332306+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, math
- domain: data-science
- platform: cross-platform
- tags: bayesian-inference, multilevel-models, stan, regression, hierarchical-models, mcmc, r-package, statistics, r

## Member repositories
- paul-buerkner/brms (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:41.332306+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:37:36.234813+00:00, confidence not recorded.
  - readme: https://github.com/paul-buerkner/brms (fetched 2026-08-28T04:04:41.332306+00:00, sha 7ea84984528f)
  - homepage: https://paulbuerkner.com/brms/ (fetched 2026-08-29T11:49:35.799100+00:00, sha 942146983b10)
  - site_page: https://paulbuerkner.com/brms/news/index.html (fetched 2026-08-29T11:49:35.808438+00:00, sha f9b1e8583e5e)
  - site_page: https://paulbuerkner.com/brms/authors.html (fetched 2026-08-29T11:49:35.813049+00:00, sha 032e0b8fed51)
- Data as of 2026-08-30T08:39:29.467469+00:00.