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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 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:
First time Bayesian modelers and ArviZ users: EABM book
@article{arviz_2019,
doi = {10.21105/joss.01143},
url = {https://doi.org/10.21105/joss.01143},
year = {2019},
publisher = {The Open Journal},
volume = {4},
number = {33},
pages = {1143},
author = {Ravin Kumar and Colin Carroll and Ari Hartikainen and Osvaldo Martin},
title = {ArviZ a unified library for exploratory analysis of Bayesian models in Python},
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.