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Author
Last Commit
Oct. 23, 2018
Created
Jul. 29, 2015

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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 official Arviz documentation can be found here https://arviz-devs.github.io/arviz/index.html

Installation

The latest 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 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 and 3.6, and depends on NumPy, SciPy, xarray, and Matplotlib.

Developing

A typical development workflow is:

  1. Install project requirements: pip install requirements.txt
  2. Install additional testing requirements: pip install requirements-dev.txt
  3. Write helpful code and tests.
  4. Verify code style: ./scripts/lint.sh
  5. Run test suite: pytest arviz/tests
  6. Make a pull request.

There is also a Dockerfile which helps for isolating build problems and local development.

  1. Install Docker for your operating system
  2. Clone this repo,
  3. Run ./scripts/start_container.sh

This should start a local docker container called arviz, as well as a Jupyter notebook server running on port 8888. The notebook should be opened in your browser automatically (you can disable this by passing --no-browser). The container will be running the code from your local copy of arviz, so you can test your changes.

Latest Releases
Alpha release
 Oct. 3 2018
Preliminary release
 Sep. 21 2018