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https://github.com/vnmabus/dcor

Distance correlation and related E-statistics in Python
https://github.com/vnmabus/dcor

distance-correlation python python2 python3 statistics

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Distance correlation and related E-statistics in Python

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README 

        
dcor

====



|tests| |docs| |coverage| |repostatus| |versions| |pypi| |conda| |zenodo|



dcor: distance correlation and energy statistics in Python.



E-statistics are functions of distances between statistical observations

in metric spaces.



Distance covariance and distance correlation are

dependency measures between random vectors introduced in [SRB07]_ with

a simple E-statistic estimator.



This package offers functions for calculating several E-statistics

such as:



- Estimator of the energy distance [SR13]_.

- Biased and unbiased estimators of distance covariance and

  distance correlation [SRB07]_.

- Estimators of the partial distance covariance and partial

  distance covariance [SR14]_.



It also provides tests based on these E-statistics:



- Test of homogeneity based on the energy distance.

- Test of independence based on distance covariance.



Installation

============



dcor is on PyPi and can be installed using :code:`pip`:



.. code::



   pip install dcor

   

It is also available for :code:`conda` using the :code:`conda-forge` channel:



.. code::



   conda install -c conda-forge dcor

   

Previous versions of the package were in the :code:`vnmabus` channel. This

channel will not be updated with new releases, and users are recommended to

use the :code:`conda-forge` channel.



Requirements

------------



dcor is available in Python 3.8 or above in all operating systems.

The package dcor depends on the following libraries:



- numpy

- numba >= 0.51

- scipy

- joblib



Citing dcor

===========



Please, if you find this software useful in your work, reference it citing the following paper:



.. code-block::

  

  @article{ramos-carreno+torrecilla_2023_dcor,

    author = {Ramos-Carreño, Carlos and Torrecilla, José L.},

    doi = {10.1016/j.softx.2023.101326},

    journal = {SoftwareX},

    month = {2},

    title = {{dcor: Distance correlation and energy statistics in Python}},

    url = {https://www.sciencedirect.com/science/article/pii/S2352711023000225},

    volume = {22},

    year = {2023},

  }



You can additionally cite the software repository itself using:



.. code-block::



  @misc{ramos-carreno_2022_dcor,

    author = {Ramos-Carreño, Carlos},

    doi = {10.5281/zenodo.3468124},

    month = {3},

    title = {dcor: distance correlation and energy statistics in Python},

    url = {https://github.com/vnmabus/dcor},

    year = {2022}

  }



If you want to reference a particular version for reproducibility, check the version-specific DOIs available in Zenodo.



Documentation

=============

The documentation can be found in https://dcor.readthedocs.io/en/latest/?badge=latest



References

==========



.. [SR13] Gábor J. Székely and Maria L. Rizzo. Energy statistics: a class of

           statistics based on distances. Journal of Statistical Planning and

           Inference, 143(8):1249 – 1272, 2013.

           URL:

           http://www.sciencedirect.com/science/article/pii/S0378375813000633,

           doi:10.1016/j.jspi.2013.03.018.

.. [SR14]  Gábor J. Székely and Maria L. Rizzo. Partial distance correlation

           with methods for dissimilarities. The Annals of Statistics,

           42(6):2382–2412, 12 2014.

           doi:10.1214/14-AOS1255.

.. [SRB07] Gábor J. Székely, Maria L. Rizzo, and Nail K. Bakirov. Measuring and

           testing dependence by correlation of distances. The Annals of

           Statistics, 35(6):2769–2794, 12 2007.

           doi:10.1214/009053607000000505.



.. |tests| image:: https://github.com/vnmabus/dcor/actions/workflows/main.yml/badge.svg

    :alt: Tests

    :scale: 100%

    :target: https://github.com/vnmabus/dcor/actions/workflows/main.yml



.. |docs| image:: https://readthedocs.org/projects/dcor/badge/?version=latest

    :alt: Documentation Status

    :scale: 100%

    :target: https://dcor.readthedocs.io/en/latest/?badge=latest

    

.. |coverage| image:: http://codecov.io/github/vnmabus/dcor/coverage.svg?branch=develop

    :alt: Coverage Status

    :scale: 100%

    :target: https://codecov.io/gh/vnmabus/dcor/branch/develop

    

.. |repostatus| image:: https://www.repostatus.org/badges/latest/active.svg

   :alt: Project Status: Active – The project has reached a stable, usable state and is being actively developed.

   :target: https://www.repostatus.org/#active

   

.. |versions| image:: https://img.shields.io/pypi/pyversions/dcor

   :alt: PyPI - Python Version

   :scale: 100%

    

.. |pypi| image:: https://badge.fury.io/py/dcor.svg

    :alt: Pypi version

    :scale: 100%

    :target: https://pypi.python.org/pypi/dcor/

    

.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/dcor

    :alt: Available in Conda

    :scale: 100%

    :target: https://anaconda.org/conda-forge/dcor

    

.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3468124.svg

    :alt: Zenodo DOI

    :scale: 100%

    :target: https://doi.org/10.5281/zenodo.3468124