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