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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
Last synced: 1 day ago
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Distance correlation and related E-statistics in Python
- Host: GitHub
- URL: https://github.com/vnmabus/dcor
- Owner: vnmabus
- License: mit
- Created: 2017-09-13T12:53:50.000Z (over 7 years ago)
- Default Branch: develop
- Last Pushed: 2024-08-31T19:11:14.000Z (4 months ago)
- Last Synced: 2024-12-14T12:04:10.523Z (9 days ago)
- Topics: distance-correlation, python, python2, python3, statistics
- Language: Python
- Homepage: https://dcor.readthedocs.io
- Size: 338 KB
- Stars: 145
- Watchers: 7
- Forks: 26
- Open Issues: 15
-
Metadata Files:
- Readme: README.rst
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
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
- joblibCiting 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=latestReferences
==========.. [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