.. _infomeasure_docs:

===========================
`infomeasure` Documentation
===========================

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       <img src="https://img.shields.io/conda/vn/conda-forge/infomeasure.svg" alt="Conda version" />
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     <a href="https://pypi.org/project/infomeasure/" style="margin: 0 10px; display: inline-block;">
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On these pages you can find documentation for `infomeasure`.

What is `infomeasure`?
--------------------

`infomeasure` is a Python library for computing information measures, such as entropy,
mutual information and conditional mutual information.
It provides a simple and efficient way to compute these measures on large datasets.
The `Reference pages <guide>`__ provide a comprehensive, theoretical background on the concepts behind these measures, while the `Demos <demos>`__ provide practical examples of how to use `infomeasure` in real-world applications.

Setup and use
-------------

To set up `infomeasure`, see the `Getting started <getting_started>`__ page, more on
the details of the inner workings can be found on the `Reference pages <guide>`__.
Furthermore, you can also find the `API documentation <api/index.html>`__.

How to cite
-----------

If you use `infomeasure` in your research, find the `CITATION.cff` file in `the repository <https://github.com/cbueth/infomeasure>`__ and cite it accordingly.
GitHub provides a "Cite this repository" button on the right side of the page for an APA and BibTeX citation.

Contributing
------------
If you want to contribute to the development of `infomeasure`, please read the
`CONTRIBUTING.md <https://github.com/cbueth/infomeasure/blob/main/CONTRIBUTING.md>`__
file.

.. toctree::
   :name: overview
   :caption: Overview
   :maxdepth: 1
   :glob:

   getting_started
   guide/index
   demos/index
   api/index
   changelog
   bibliography