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Exploratory analysis of Bayesian models with Python
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README.md

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ArviZ

ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, model checking, comparison and diagnostics.

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 master 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 3.5, 3.6 and 3.7, 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,
	title = {{ArviZ} a unified library for exploratory analysis of {Bayesian} models in {Python}},
	author = {Kumar, Ravin and Colin, Carroll and Hartikainen, Ari and Martin, Osvaldo A.},
	journal = {The Journal of Open Source Software},
	year = {2019},
	doi = {10.21105/joss.01143},
	url = {http://joss.theoj.org/papers/10.21105/joss.01143},
}

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

Sponsors

NumFOCUS

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