arviz-stats
Statistical computation and diagnostics for ArviZ.
Decision gist · record as of 2026-08-14
Yes, if you work with Bayesian models and need standard diagnostics and posterior analysis. The low dependency footprint (numpy, scipy only) and active maintenance make it a practical choice. Install with the optional xarray extra for full feature access, or use the minimal version if you're integrating into a library with strict dependencies.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later.
- Optional xarray dependency unlocks full feature set; minimal install provides only array-based functions.
- Low friction installation with only numpy and scipy as runtime dependencies.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache License (permissive), allowing broad use in commercial and private projects with minimal restrictions.
last release 2026-08-11 (3 days) · last repo commit 2026-08-11 · 18 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 730,936 downloads/mo, #5,208 on PyPI
Alternatives
Verify before relying
pip install arviz-stats
import arviz_stats
# Use statistical functions for Bayesian model diagnostics
# Functions accept numpy arrays or scipy distributions- Specific diagnostic functions and their names beyond generic 'posterior analysis, model checking, comparison'
- Whether the package is actively used by the broader Bayesian modeling community or primarily internal to ArviZ ecosystem
- Performance characteristics or scalability limits for large posterior samples
What it is and what it does
arviz-stats is the statistical computation subpackage within the ArviZ ecosystem for Bayesian model analysis. It handles the core diagnostics and statistical summaries needed after running Bayesian inference, such as convergence checks, posterior summaries, and model comparison metrics. The package is designed as a modular component that can be used standalone with just numpy and scipy, or integrated into the broader ArviZ suite with xarray support for richer data structures.
The package targets both researchers doing exploratory Bayesian analysis and library developers who want to compute diagnostics without adopting the full ArviZ dependency tree. It supports current Python versions (3.12+) and maintains active development with regular updates.
Use it for
- Computing convergence diagnostics (Rhat, effective sample size) for MCMC chains after inference
- Generating posterior predictive checks and model comparison statistics for Bayesian model validation
- Integrating Bayesian diagnostics into custom inference pipelines without adopting xarray
- Analyzing posterior samples from Stan, PyMC, or other Bayesian inference libraries
- Building downstream tools that need standardized Bayesian statistical summaries
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with Bayesian models and need standard diagnostics and posterior analysis.
The low dependency footprint (numpy, scipy only) and active maintenance make it a practical choice. Install with the optional xarray extra for full feature access, or use the minimal version if you're integrating into a library with strict dependencies.
Install
arviz-stats on PyPI
Before you install
Low friction installation with only numpy and scipy as runtime dependencies. Active maintenance with a release 3 days old and recent commits.
Requires Python 3.12 or later. Optional xarray dependency unlocks full feature set; minimal install provides only array-based functions.
License in practice
Licensed under Apache License (permissive), allowing broad use in commercial and private projects with minimal restrictions.
Quickstart
pip install arviz-stats
import arviz_stats
# Use statistical functions for Bayesian model diagnostics
# Functions accept numpy arrays or scipy distributionsVerify before relying
- Specific diagnostic functions and their names beyond generic 'posterior analysis, model checking, comparison'
- Whether the package is actively used by the broader Bayesian modeling community or primarily internal to ArviZ ecosystem
- Performance characteristics or scalability limits for large posterior samples
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 730,936 / month, #5,208 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_stats-1.3.0-py3-none-any.whl
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