# arviz-devs/arviz

Exploratory analysis of Bayesian models with Python

Repository: https://github.com/arviz-devs/arviz
Canonical: https://ross.abutalabs.com/products/arviz
Homepage: https://python.arviz.org
Language: TeX
License: Apache-2.0
License Family: permissive
Topics: python, bayesian, closember
Last push: 2026-08-17T16:23:06+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 85, longevity 100
- inputs: {"age_days": 4053, "days_push": 16, "days_rel": 22, "gap_med": 50.5, "n_releases_24m": 11}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1849, forks 503 (observed 2026-08-28T04:05:44.103368+00:00)

## What it is
ArviZ is a Python package for exploratory analysis of Bayesian models, providing posterior analysis, diagnostics, model comparison, plotting, and data storage. It also has a Julia wrapper and a modular ArviZ-verse of subpackages (arviz-base, arviz-stats, arviz-plots).

## Use cases
- plot posterior distributions from MCMC samples
- compute R-hat and effective sample size diagnostics
- compare Bayesian models with LOO and WAIC
- run posterior predictive checks
- store and share Bayesian inference results in InferenceData format
- visualize trace plots and forest plots
- summarize MCMC chains from PyMC or Stan

## When to choose
- you work with Bayesian models in Python and need diagnostics or plots
- you use PyMC, Stan, NumPyro, or other MCMC tools and want a common analysis layer
- you need standardized storage for inference results
- you want publication-quality Bayesian visualization

## When to avoid
- you need to fit models yourself rather than analyze fitted ones
- you want a general-purpose plotting library unrelated to Bayesian statistics
- you need frequentist-only statistical analysis

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, data-science, analytics, benchmarking
- domain: data-science, data-visualization
- platform: python, cross-platform
- tags: bayesian, mcmc, posterior-analysis, diagnostics, model-comparison, plots, xarray, statistics, bayesian-inference

## Member repositories
- arviz-devs/arviz (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:44.103368+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 2026-08-30T03:17:16.709462+00:00, confidence not recorded.
  - readme: https://github.com/arviz-devs/arviz (fetched 2026-08-28T04:05:44.103368+00:00, sha 26b1e280670e)
- Data as of 2026-08-30T08:39:29.467469+00:00.