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-stats is the subpackage in charge of the statistical and diagnostics computations.
ArviZ also has a Julia wrapper available ArviZ.jl.
The ArviZ documentation can be found in the official docs. Here are some quick links for common scenarios:
- First time Bayesian modelers and ArviZ users: EABM book
- First time ArviZ users, already familiar with Bayesian modeling: overview notebook or example gallery
- ArviZ 0.x user: migration guide
- ArviZ-verse documentation:
- arviz-base
- arviz-stats (this package)
- arviz-plots
ArviZ is available for installation from PyPI. The latest stable version can be installed using pip:
pip install "arviz-stats[xarray]"
Note that it is also possible to install arviz-stats without the [xarray]. Doing that will install a minimal package, which only depends on numpy and scipy. Consequently, the functions that take arrays as inputs will be available, but many features won’t be available.
This is mostly aimed at advanced users or libraries that want to use ArviZ to compute diagnostics and statistical summaries but don’t want to depend on xarray.
The latest development version can be installed from the main branch using pip:
pip install git+git://github.com/arviz-devs/arviz-stats.git
Another option is to clone the repository and install using git and setuptools:
git clone https://github.com/arviz-devs/arviz-stats.git
cd arviz-stats
python setup.py install
If you use ArviZ and want to cite it please use
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}}
ArviZ is a community project and welcomes contributions. Additional information can be found in the contributing guide
ArviZ wishes to maintain a positive community. Additional details can be found in the Code of Conduct
ArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate here.
The ArviZ project website has more information about each sponsor and the support they provide.