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Overview of mgc

mgc (pronounced "Magic") is an open-source software package for independence and k-sample testing.

Motivation

With the increase in the amount of data in many fields, a method to consistently and efficiently decipher relationships within high dimensional data sets is important. Because many modern datasets are multivariate, univariate independence tests are not applicable. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. mgc is an extensive Python library that includes many state of the art multivariate independence testing procedures using a common interface. The package is easy-to-use and is flexible enough to enable future extensions.

Python

Python is a powerful programming language that allows concise expressions of network algorithms. Python has a vibrant and growing ecosystem of packages that mgc uses to provide more features such as numerical linear algebra and plotting. In order to make the most out of mgc you will want to know how to write basic programs in Python. Among the many guides to Python, we recommend the Python documentation.

Free software

mgc is free software; you can redistribute it and/or modify it under the terms of the :doc:`MIT </license>`. We welcome contributions. Join us on GitHub.

History

mgc is a rebranding of mgcpy, which was founded in September 2018. The original version was designed and written by Satish Palaniappan, Sambit Panda Junhao Xiong, Sandhya Ramachandran, and Ronak Mehtra. This new version was written by Sambit Panda.

Documentation

.. toctree::
   :maxdepth: 1

   install
   reference/index
   contributing
   license

.. toctree::
   :maxdepth: 1
   :caption: Useful Links

   mgc @ GitHub <https://github.com/neurodata/mgc>
   mgc @ PyPi <https://pypi.org/project/mgc/>
   Issue Tracker <https://github.com/neurodata/mgc/issues>


Indices and tables

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