arviz
Expose features from _ArviZverse_ refactored packages together in the ``arviz`` namespace.
Decision gist · record as of 2026-08-14
Yes. ArviZ is a mature, actively maintained tool (release 3 days old, 1846 repository stars) with no known vulnerabilities, permissive licensing, and low install friction. It is essential for anyone working with Bayesian inference in Python who needs systematic posterior analysis and diagnostics beyond what individual samplers provide.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later.
- Low friction installation as a pure Python wheel.
- Actively maintained with a release 3 days old and recent commits; supports current Python versions (3.12, 3.13, 3.14).
License · maintenance · safety
permissive license (permissive) — Licensed under Apache (permissive), imposing no significant restrictions on use or redistribution.
last release 2026-08-11 (3 days) · last repo commit 2026-08-11 · 1,846 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,940,649 downloads/mo, #2,440 on PyPI
Alternatives
Verify before relying
pip install arviz
import arviz as az
# Load and analyze Bayesian model results
data = az.from_pymc3(trace=your_trace, prior=your_prior)
az.plot_posterior(data)- Whether arviz_base, arviz_stats, and arviz_plots are required at runtime or optional modular components.
- Specific Bayesian sampling frameworks (PyMC, Stan, etc.) that ArviZ integrates with beyond what the fact sheet states.
What it is and what it does
ArviZ is a modular library for analyzing Bayesian models after inference. It sits downstream of sampling tools (like PyMC or Stan) and provides a unified interface for posterior diagnostics, visualization, and model comparison. The package exposes features from three refactored subpackages—arviz_base, arviz_stats, and arviz_plots—together in the arviz namespace, allowing users to work with inference results in a consistent way regardless of which sampler generated them.
The library is designed for exploratory analysis: inspecting posterior distributions, checking convergence, comparing competing models, and validating inference quality. It handles data storage and transformation, making it straightforward to load traces from different Bayesian frameworks and apply a common suite of diagnostics and plots. Development is active (last commit 2026-08-11) and the package supports modern Python versions.
Use it for
- Inspect posterior distributions and convergence diagnostics after running MCMC or variational inference.
- Compare competing Bayesian models using built-in comparison metrics and visualizations.
- Validate inference quality and detect sampling issues through standardized diagnostic plots.
- Store and reload Bayesian inference results in a framework-agnostic format.
- Generate publication-quality plots of Bayesian analysis results for reports or papers.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ArviZ is a mature, actively maintained tool (release 3 days old, 1846 repository stars) with no known vulnerabilities, permissive licensing, and low install friction. It is essential for anyone working with Bayesian inference in Python who needs systematic posterior analysis and diagnostics beyond what individual samplers provide.
Install
arviz on PyPI
Before you install
Low friction installation as a pure Python wheel. Actively maintained with a release 3 days old and recent commits; supports current Python versions (3.12, 3.13, 3.14).
Requires Python 3.12 or later.
License in practice
Licensed under Apache (permissive), imposing no significant restrictions on use or redistribution.
Quickstart
pip install arviz
import arviz as az
# Load and analyze Bayesian model results
data = az.from_pymc3(trace=your_trace, prior=your_prior)
az.plot_posterior(data)Verify before relying
- Whether arviz_base, arviz_stats, and arviz_plots are required at runtime or optional modular components.
- Specific Bayesian sampling frameworks (PyMC, Stan, etc.) that ArviZ integrates with beyond what the fact sheet states.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesarviz_basearviz_statsarviz_plots |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,940,649 / month, #2,440 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: arviz-1.3.0-py3-none-any.whl
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See also arviz-base · arviz-plots · arviz-stats · preliz · corner · pystan · emcee · nutpie · bayesian-optimization · pymc3