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https://arviz-devs.github.io/arviz/
Exploratory analysis of Bayesian models with Python
https://arviz-devs.github.io/arviz/
bayesian closember python
Last synced: 30 days ago
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Exploratory analysis of Bayesian models with Python
- Host: GitHub
- URL: https://arviz-devs.github.io/arviz/
- Owner: arviz-devs
- License: apache-2.0
- Created: 2015-07-29T11:51:10.000Z (over 9 years ago)
- Default Branch: main
- Last Pushed: 2024-10-08T21:42:17.000Z (2 months ago)
- Last Synced: 2024-10-30T04:54:33.883Z (about 1 month ago)
- Topics: bayesian, closember, python
- Language: Python
- Homepage: https://python.arviz.org
- Size: 117 MB
- Stars: 1,600
- Watchers: 48
- Forks: 401
- Open Issues: 182
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Governance: GOVERNANCE.md
Awesome Lists containing this project
- awesome-python-tools - ArviZ
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README
[](https://badge.fury.io/py/arviz)
[](https://dev.azure.com/ArviZ/ArviZ/_build/latest?definitionId=1&branchName=main)
[](https://codecov.io/gh/arviz-devs/arviz)
[](https://github.com/ambv/black)
[](https://gitter.im/arviz-devs/community)
[](https://doi.org/10.21105/joss.01143) [](https://doi.org/10.5281/zenodo.2540945)
[](https://numfocus.org)
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](https://julia.arviz.org/).
## Documentation
The ArviZ documentation can be found in the [official docs](https://python.arviz.org/en/latest/index.html).
First time users may find the [quickstart](https://python.arviz.org/en/latest/getting_started/Introduction.html)
to be helpful. Additional guidance can be found in the
[user guide](https://python.arviz.org/en/latest/user_guide/index.html).
## Installation
### Stable
ArviZ is available for installation from [PyPI](https://pypi.org/project/arviz/).
The latest stable version can be installed using pip:
```
pip install arviz
```
ArviZ is also available through [conda-forge](https://anaconda.org/conda-forge/arviz).
```
conda install -c conda-forge arviz
```
### Development
The latest development version can be installed from the main branch using pip:
```
pip install git+git://github.com/arviz-devs/arviz.git
```
Another option is to clone the repository and install using git and setuptools:
```
git clone https://github.com/arviz-devs/arviz.git
cd arviz
python setup.py install
```
-------------------------------------------------------------------------------
## [Gallery](https://python.arviz.org/en/latest/examples/index.html)
## Dependencies
ArviZ is tested on Python 3.10, 3.11 and 3.12, and depends on NumPy, SciPy, xarray, and Matplotlib.
## Citation
If you use ArviZ and want to cite it please use [](https://doi.org/10.21105/joss.01143)
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}
}
```
## Contributions
ArviZ is a community project and welcomes contributions.
Additional information can be found in the [Contributing Readme](https://github.com/arviz-devs/arviz/blob/main/CONTRIBUTING.md)
## Code of Conduct
ArviZ wishes to maintain a positive community. Additional details
can be found in the [Code of Conduct](https://github.com/arviz-devs/arviz/blob/main/CODE_OF_CONDUCT.md)
## Donations
ArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate [here](https://numfocus.org/donate-to-arviz).
## Sponsors
[](https://numfocus.org)