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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:
It also provides tests based on these E-statistics:
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.
dcor is available in Python 3.8 or above in all operating systems. The package dcor depends on the following libraries:
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.
The documentation can be found in https://dcor.readthedocs.io/en/latest/?badge=latest
.. [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