Metadata-Version: 2.1
Name: dcor
Version: 0.6
Summary: dcor: distance correlation and energy statistics in Python.
Author-email: Carlos Ramos Carreño <vnmabus@gmail.com>
Maintainer-email: Carlos Ramos Carreño <vnmabus@gmail.com>
License: MIT License
        
        Copyright (c) 2017 Carlos Ramos Carreño
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: homepage, https://github.com/vnmabus/dcor
Project-URL: documentation, https://dcor.readthedocs.io
Project-URL: repository, https://github.com/vnmabus/dcor
Keywords: distance correlation,distance covariance,energy distance,e-statistic,dependency measure,homogeneity,independence
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: numba (>=0.51)
Requires-Dist: scipy
Requires-Dist: joblib
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'
Requires-Dist: pytest-subtests ; extra == 'test'
Requires-Dist: numpy (>=1.22) ; extra == 'test'
Requires-Dist: numba (>=0.56) ; extra == 'test'

dcor
====

|tests| |docs| |coverage| |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

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
    
.. |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
