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.
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 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
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
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
