Skip to main content

Exploratory analysis of Bayesian models

Project description

PyPI version Azure Build Status codecov Code style: black Gitter chat DOI DOI Powered by NumFOCUS

ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. 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. First time users may find the quickstart to be helpful. Additional guidance can be found in the user guide.

Installation

Stable

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

pip install arviz

ArviZ is also available through conda-forge.

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

Ridge plot Parallel plot Trace plot Density plot
Posterior plot Joint plot Posterior predictive plot Pair plot
Energy Plot Violin Plot Forest Plot Autocorrelation Plot

Dependencies

ArviZ is tested on Python 3.7, 3.8 and 3.9, and depends on NumPy, SciPy, xarray, and Matplotlib.

Citation

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

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

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

NumFOCUS

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

arviz-0.12.1.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

arviz-0.12.1-py3-none-any.whl (1.6 MB view details)

Uploaded Python 3

File details

Details for the file arviz-0.12.1.tar.gz.

File metadata

  • Download URL: arviz-0.12.1.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for arviz-0.12.1.tar.gz
Algorithm Hash digest
SHA256 57d80eacc51909f18e6ab63c96a6b02227c3b077c5ffa406d5f4dabe03b8f019
MD5 13f7b65e640494af8f8f5db58144ca51
BLAKE2b-256 db6beb671298f172ccb384bd40eff92e8c8a6f82fb2777b616fd12674a0dbc3c

See more details on using hashes here.

File details

Details for the file arviz-0.12.1-py3-none-any.whl.

File metadata

  • Download URL: arviz-0.12.1-py3-none-any.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for arviz-0.12.1-py3-none-any.whl
Algorithm Hash digest
SHA256 95a2b94995e30683b52c30f82326eed1544d057244bea468d421d5c42ed1f245
MD5 f1852555d86f8e5917517811ac8233fd
BLAKE2b-256 95c0dd9b4bf085d974de1c6d5fffea8e25153e8208ece6b363fa3a9263ed7e51

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page