.. GerryChain documentation master file, created by
   sphinx-quickstart on Mon Jun 11 14:55:33 2018.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

==========
GerryChain
==========

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    :target: https://circleci.com/gh/mggg/GerryChain
    :alt: Build Status
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    :alt: Code Coverage
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    :alt: Documentation Status
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    :alt: PyPI Package


GerryChain is a library for using `Markov Chain Monte Carlo`_ methods
to study the problem of political redistricting. Development of the
library began during the 2018 Voting Rights Data Institute (VRDI).

The project is in active development in the `mggg/GerryChain`_ GitHub
repository, where `bug reports and feature requests`_, as well as 
`contributions`_, are welcome.


.. _`Markov Chain Monte Carlo`: https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo
.. _`Metric Geometry and Gerrymandering Group`: https://www.mggg.org/
.. _`MGGG/GerryChain`: https://github.com/mggg/GerryChain
.. _`bug reports and feature requests`: https://github.com/mggg/gerrychain/issues
.. _`contributions`: https://github.com/mggg/gerrychain/pulls


.. include:: user/install.rst

.. toctree::
    :caption: User Guide
    :maxdepth: 2

    user/intro
    user/install
    user/quickstart
    user/recom
    user/partitions
    user/updaters
    user/data
    user/geometries
    user/optimizers

We also highly recommend the resources prepared by Daryl R. DeFord of
MGGG for the 2019 MIT IAP course `Computational Approaches for Political Redistricting`_.


.. _`Computational Approaches for Political Redistricting`: https://people.csail.mit.edu/ddeford//CAPR.php


.. toctree::
    :caption: API Reference
    :maxdepth: 2

    api


.. toctree::
    :caption: Topics
    :maxdepth: 1

    topics/reproducibility
    topics/tools
    topics/contributing
    topics/reporting


.. toctree::
    :caption: Index
    :maxdepth: 4

    full_ref