(support)=
If you have found ArviZ useful in your work, research, or company, consider supporting the project in any of the ways described in this section.
If you or your company have the means to do so, consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of ArviZ’s open source software, documentation, and community.
:::{include} donate.md :::
(cite)=
If you use ArviZ in your scientific work, you can cite it using
Here is the citation in BibTeX format:
@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}
}Please also consider citing ArviZ's {ref}arviz:arviz-dependencies and
the inference library used to build and fit the model.
(tidelift)=
ArviZ is now available as part of the Tidelift Subscription!
Tidelift is working with ArviZ and the maintainers of thousands of other open source projects to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while contributing financially to ArviZ.
::::{div} sd-d-flex-row sd-align-major-center :::{button-link} https://tidelift.com/subscription/pkg/pypi-arviz?utm_source=pypi-arviz&utm_medium=referral&utm_campaign=enterprise :color: warning
Learn more ::: ::::