HiDimStat 0.3.0
High-dimensional statistical inference tools for Python
Requires Python: <4,>=3.10
Keywords: statistic, high dimension, variable importance

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.. ## Reference to the CI status
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   :target: https://github.com/mind-inria/hidimstat/actions/workflows/ci.yml?query=branch%3Amain
.. |CircleCI/Documentation| image:: https://circleci.com/gh/mind-inria/hidimstat.svg?style=shield
   :target: https://circleci.com/gh/mind-inria/hidimstat?branch=main
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   :target: https://codecov.io/github/mind-inria/hidimstat
.. ## Distribution python
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   :target: https://pypi.org/project/hidimstat/
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   :target: https://opensource.org/license/bsd-3-clause
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.. # Add minimal dependency of the main packages
  ## This need to update in same time that pyproject.toml
.. |PythonMinVersion| replace:: 3.10
.. |JoblibMinVersion| replace:: 1.2
.. |NumPyMinVersion| replace:: 1.25
.. |PandasMinVersion| replace:: 2.0
.. |SklearnMinVersion| replace:: 1.4
.. |SciPyMinVersion| replace:: 1.6
.. ## for plotting and for examples
.. |MatplotlibMinVersion| replace:: 3.9.0
.. |SeabornMinVersion| replace:: 0.9.0

=================================================================
HiDimStat: High-dimensional statistical inference tool for Python
=================================================================

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|Linter&Tests| |CircleCI/Documentation| |CodeCov| |Black|

|PyPi| |PyPi_download| |PythonVersion| |Latest release|

|License| 

.. # Short description of the library

The HiDimStat package provides statistical inference methods to solve the problem
of variable importance evaluation in the context of predictive model using high-dimensional and spatially structured data.

.. # Add useful links

If you like the package, spread the word and ⭐ our `official repository 
<https://github.com/mind-inria/hidimstat>`_!

Visit our website, https://hidimstat.github.io/, for more information.

..
  ## TODO: Add short citation when this will be ready
  If you use HiDimStat for your published research, we kindly ask you to :ref:`cite<citation>` our article:
  short reference

Find your important variables in your data with the help of 
`our examples <https://hidimstat.github.io/dev/auto_examples/index.html>`_.

If you have any problems, please report them to the `GitHub issue tracker <https://github.com/mind-inria/hidimstat/issues>`_ 
or contribute to the library by opening a pull request.

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

Dependencies
^^^^^^^^^^^^

.. # Add dependency of the project
  TODO Need to match with pyproject.toml

HiDimStat requires:

- Python (>= |PythonMinVersion|)
- joblib (>= |JoblibMinVersion|)
- NumPy (>= |NumPyMinVersion|)
- Pandas (>= |PandasMinVersion|)
- Scikit-learn (>= |SklearnMinVersion|)
- SciPy (>= |SciPyMinVersion|)

HiDimStat's plotting capabilities require Matplotlib (>= |MatplotlibMinVersion|).

To run the examples, Matplotlib (>= |MatplotlibMinVersion|) and seaborn (>=
|SeabornMinVersion|) are required.

User installation
^^^^^^^^^^^^^^^^^

.. # Add the instruction for installation
  TODO add conda when it will be accessible

HiDimStat can easily be installed via ``pip``. For more installation information,
see the `installation instructions <https://hidimstat.github.io/dev/index.html#installation>`_.
.. code-block:: bash
  
  pip install -U hidimstat 

Contribute
----------

.. # Add short description for contribution to the library

The best way to support the development of HiDimStat is to spread the word!

HiDimStat aims to be supported by an active community, and we welcome 
contributions to our code and documentation.

For bug reports, feature requests, documentation improvements, or other issues, 
you can create a `GitHub issue <https://github.com/mind-inria/hidimstat/issues>`_.

If you want to contribute directly to the library, check the 
`how to contribute <https://hidimstat.github.io/dev/how_to_contribute.html/>`_ page 
on the website for more information.

Contact us
----------

.. # Add a way to contact maintainers 
  TODO this needs to be updated when there is a change of maintainers

| Currently, this library is supported by the `INRIA <https://www.inria.fr/en>`_ 
  team `MIND <https://www.inria.fr/fr/mind>`_.
| If you want to report a problem or suggest an enhancement, we would love for you 
  to `open an issue <https://github.com/mind-inria/hidimstat/issues/new>`_ at 
  this GitHub repository so we can address it quickly.
| For less formal discussions or to exchange ideas, you can contact the main 
  contributors:

+-------------------+-------------------+-------------------+-------------------+
|   Lionel Kusch    | Bertrand Thirion  |  Joseph Paillard  | Angel Reyero Lobo |
+-------------------+-------------------+-------------------+-------------------+
|    |avatar LK|    |    |avatar BT|    |     |avatar JP|   |    |avatar AR|    |
+-------------------+-------------------+-------------------+-------------------+

.. |avatar LK| image:: https://avatars.githubusercontent.com/u/17182418?v=4 
  :target: https://github.com/lionelkusch
.. |avatar BT| image:: https://avatars.githubusercontent.com/u/234454?v=4
  :target: https://github.com/bthirion
.. |avatar JP| image:: https://avatars.githubusercontent.com/u/56166877?v=4 
  :target: https://github.com/jpaillard
.. |avatar AR| image:: https://avatars.githubusercontent.com/u/79699169?v=4 
  :target: https://github.com/AngelReyero

Citation
--------

If you use a HiDimStat method for your research, you'll find the associated 
reference paper in the method description, and we recommend that you cite it.

..
  TODO add the section for citing the library once a Zenodo repository is made
  or a paper is published.

If you publish a paper using HiDimStat, please contact us or open an issue! 
We would love to hear about your work and help you promote it.

Acknowledgments
---------------

This project has been funded by `Labex DigiCosme <https://anr.fr/ProjetIA-11-LABX-0045>`_
(ANR-11-LABEX-0045-DIGICOSME) as part of the program 
`Investissement d’Avenir <https://anr.fr/ProjetIA-11-IDEX-0003>`_ 
(ANR-11-IDEX-0003-02), by the `Fast Big project <https://anr.fr/Projet-ANR-17-CE23-0011>`_
(ANR-17-CE23-0011), by the `KARAIB AI Chair <https://anr.fr/Projet-ANR-20-CHIA-0025>`_ 
(ANR-20-CHIA-0025-01), and by the `VITE project <https://anr.fr/Projet-ANR-23-CE23-0016>`_ (ANR-23-CE23-0016).
This study has also been supported by the European Union’s Horizon 2020 research and innovation program 
as part of the program `Human Brain Project SGA3 <https://cordis.europa.eu/project/id/945539>`_
(Grant Agreement No. 945539) and `EBRAIN-Health <https://cordis.europa.eu/project/id/101058516>`_ 
(Grant Agreement No. 101058516).