=============
BayesPowerlaw
=============

*Written by Kristina Grigaityte.*

.. image:: tweet_powerlaw.png
   :height: 260px
   :width: 380px
.. image:: tweet_posterior.png
   :height: 260px
   :width: 380px


BayesPowerlaw is a Python package that fits a single or a mixture of power law distributions to data using a Bayesian inference approach. Posterior distributions of parameters are numerically determined 
by Markov chain Monte Carlo sampling. In addition, the package provides capability for power law simulations, maximum likelihood estimation, and data plotting.

Installation
------------

BayesPowerlaw can be installed from
`PyPI <https://pypi.python.org/pypi/BayesPowerlaw>`_ using the pip package manager (version 9.0.0 or higher). At the command line::

    pip install BayesPowerlaw

The code for BayesPowerlaw is open source and available on
`GitHub <https://github.com/AtwalLab/BayesPowerlaw>`_.


Quick Start
-----------

To make the figures shown above, type this from within Python::

   import BayesPowerlaw as bp
   bp.demo()

Resources
---------

.. toctree::

   tutorial
   documentation

Contact
-------

For technical assistance or to report bugs, please 
contact `Kristina Grigaityte <kgrigait@cshl.edu>`_.

For general correspondence, please 
contact `Gurinder (Mickey) Atwal <atwal@cshl.edu>`_.


Indices and tables
------------------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`