Skip to main content

arviz-base

Run tests External integration tests codecov Powered by NumFOCUS

ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.

arviz-base is the subpackage in charge of converters and base manipulation.

ArviZ in other languages

ArviZ also has a Julia wrapper available ArviZ.jl.

Documentation

The ArviZ documentation can be found in the official docs. Here are some quick links for common scenarios:

Installation

Stable

ArviZ is available for installation from PyPI. The latest stable version can be installed using pip:

pip install "arviz-base"

Development

The latest development version can be installed from the main branch using pip:

pip install git+https://github.com/arviz-devs/arviz-base.git

Another option is to clone the repository and install using git and setuptools:

git clone https://github.com/arviz-devs/arviz-base.git
cd arviz-base
python setup.py install

Citation

If you use ArviZ and want to cite it please use DOI

Here is the citation in BibTeX format

@article{Martin2026,
doi = {10.21105/joss.09889},
url = {https://doi.org/10.21105/joss.09889},
year = {2026},
publisher = {The Open Journal},
volume = {11},
number = {119},
pages = {9889},
author = {Martin, Osvaldo A. and Abril-Pla, Oriol and Deklerk, Jordan and Axen, Seth D. and Carroll, Colin and Hartikainen, Ari and Vehtari, Aki},
title = {ArviZ: a modular and flexible library for exploratory analysis of Bayesian models},
journal = {Journal of Open Source Software}}

Contributions

ArviZ is a community project and welcomes contributions. Additional information can be found in the contributing guide

Code of Conduct

ArviZ wishes to maintain a positive community. Additional details can be found in the Code of Conduct

Donations

ArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate here.

Sponsors and Institutional Partners

Aalto University FCAI NumFOCUS

The ArviZ project website has more information about each sponsor and the support they provide.

Release files for arviz-base 1.3.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for arviz-base 1.3.1
File Size Uploaded
arviz_base-1.3.1.tar.gz 1.4 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for arviz-base 1.3.1
File Interpreter ABI Platform
arviz_base-1.3.1-py3-none-any.whl Python 3 none any Details

Total release size: 2.8 MB

Release files / arviz_base-1.3.1.tar.gz

Download URL arviz_base-1.3.1.tar.gz
Size 1.4 MB
Tags Source
SHA-256 checksum
How to use checksums
f83c49d1842ca93821d38bc03f1d75df18d56d507694804a07ce6c1b3bbf2d6c
BLAKE2b-256 checksum
How to use checksums
d1e64cbf049a330a6b36cb4c2ed6913564965d397e30304b1e1ac13067630d26
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 25, 2026.

Transparency log

Release files / arviz_base-1.3.1-py3-none-any.whl

Download URL arviz_base-1.3.1-py3-none-any.whl
Size 1.4 MB
Tags Python 3
SHA-256 checksum
How to use checksums
4b4d60807f0e136af295a3c64aa13d87d3b7ba78b54acb015f6cb515ffd4b8fd
BLAKE2b-256 checksum
How to use checksums
348f1cdf6dbab034fdf9fea31c05599497b1d14e2f62d51a6045c1a647d93af4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 25, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

1.3.1 This release

2 release files

1.3.0

2 release files

1.2.0

2 release files

1.1.0

2 release files

1.0.0

2 release files

0.8.2

2 release files

0.8.1

2 release files

0.8.0

2 release files

0.7.0

2 release files

0.6.0

2 release files

0.5.0

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page