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

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 usage documentation.

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 3.6, 3.7 and 3.8, 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-0.11.3.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

arviz-0.11.3-py3-none-any.whl (1.6 MB view details)

Uploaded Python 3

File details

Details for the file arviz-0.11.3.tar.gz.

File metadata

  • Download URL: arviz-0.11.3.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for arviz-0.11.3.tar.gz
Algorithm Hash digest
SHA256 1f31704196b5f3c52fc74849381fb57bbfa4df4a60e7060c8cddc9f4b6927ef8
MD5 8129e826dab8877a7bc33f48607da795
BLAKE2b-256 0520bce3f6fdf4a54910c1bd023b473cd3eabf606151a4b50fbe900475899dd8

See more details on using hashes here.

File details

Details for the file arviz-0.11.3-py3-none-any.whl.

File metadata

  • Download URL: arviz-0.11.3-py3-none-any.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for arviz-0.11.3-py3-none-any.whl
Algorithm Hash digest
SHA256 f1957db38855dc8cad5e7562fd9a48089842626b5fc353fd6ae61a46b8517638
MD5 05481a6c440056192b1a10efcf83b33b
BLAKE2b-256 26d1d85af92469d80d18d831250e9b1abaad754e81edb5bf148ed9435b5b47ea

See more details on using hashes here.

Supported by

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