dcor

Distance correlation and related E-statistics in Python

https://github.com/vnmabus/dcor

Science Score: 67.0%

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CITATION.cff file
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DOI references
Found 8 DOI reference(s) in README
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Low similarity (14.5%) to scientific vocabulary

Keywords

distance-correlation python python2 python3 statistics

Last synced: 4 months ago · JSON representation ·

Repository

Distance correlation and related E-statistics in Python

Basic Info

Host: GitHub
Owner: vnmabus
License: mit
Language: Python
Default Branch: develop
Homepage: https://dcor.readthedocs.io
Size: 338 KB

Statistics

Stars: 149
Watchers: 5
Forks: 27
Open Issues: 16
Releases: 8

Topics

distance-correlation python python2 python3 statistics

Created over 8 years ago · Last pushed over 1 year ago

Metadata Files

Readme Contributing License Code of conduct Citation

README.rst

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

Owner

Name: Carlos Ramos Carreño
Login: vnmabus
Kind: user
Location: Madrid, Spain

Repositories: 4
Profile: https://github.com/vnmabus

Software engineer and mathematician. PhD student in Machine Learning at Universidad Autónoma de Madrid.

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: "Ramos-Carreño"
    given-names: "Carlos"
    orcid: "https://orcid.org/0000-0003-2566-7058"
    affiliation: "Universidad Autónoma de Madrid"
    email: vnmabus@gmail.com
title: "dcor: distance correlation and energy statistics in Python"
date-released: 2022-03-24
doi: 10.5281/zenodo.3468124
url: "https://github.com/vnmabus/dcor"
license: MIT
keywords:
  - "distance correlation"
  - "distance covariance"
  - "energy distance"
  - Python
identifiers:
  - description: "This is the collection of archived snapshots of all versions of dcor"
    type: doi
    value: 10.5281/zenodo.3468124
  - description: "This is the archived snapshot of version 0.3 of dcor"
    type: doi
    value: 10.5281/zenodo.3468125
  - description: "This is the archived snapshot of version 0.4 of dcor"
    type: doi
    value: 10.5281/zenodo.3779356
  - description: "This is the archived snapshot of version 0.5 of dcor"
    type: doi
    value: 10.5281/zenodo.3996697
  - description: "This is the archived snapshot of version 0.6 of dcor"
    type: doi
    value: 10.5281/zenodo.7484447
preferred-citation:
  type: article
  title: "dcor: Distance correlation and energy statistics in Python"
  authors:
    - family-names: "Ramos-Carreño"
      given-names: "Carlos"
      orcid: "https://orcid.org/0000-0003-2566-7058"
      affiliation: "Universidad Autónoma de Madrid"
      email: vnmabus@gmail.com
    - family-names: "Torrecilla"
      given-names: "José L."
      orcid: "https://orcid.org/0000-0003-3719-5190"
      affiliation: "Universidad Autónoma de Madrid"
      email: joseluis.torrecilla@uam.es
  date-published: 2023-02-02
  abstract: "This article presents dcor, an open-source Python package dedicated to distance correlation and other statistics related to energy distance. These energy statistics include distances between distributions and the associated tests for homogeneity and independence. Some of the most efficient algorithms for the estimation of these measures have been implemented relying on optimization techniques such as vectorization, compilation, and parallelization. The performance of these estimators is evaluated by comparison with alternative implementations in other packages. The package is also designed to be compatible with the packages conforming the scientific Python ecosystem. With that purpose in mind, dcor is an early adopter of the Python array API standard."
  doi: 10.1016/j.softx.2023.101326
  institution:
    name: "Universidad Autónoma de Madrid"
  issn: "2352-7110"
  issue-date: "2023-05-01"
  journal: "SoftwareX"
  keywords:
    - "Distance correlation"
    - "Energy distance"
    - "Energy statistics"
    - "Hypothesis testing"
    - "Python"
  languages:
    - en
  license: CC-BY-4.0
  publisher:
    name: "Elsevier"
  url: "https://www.sciencedirect.com/science/article/pii/S2352711023000225"
  volume: 22

GitHub Events

Total

Watch event: 8
Issue comment event: 7
Pull request review event: 3
Pull request review comment event: 2
Pull request event: 3
Fork event: 2

Last Year

Watch event: 8
Issue comment event: 7
Pull request review event: 3
Pull request review comment event: 2
Pull request event: 3
Fork event: 2

Committers

Last synced: 7 months ago

All Time

Total Commits: 269
Total Committers: 5
Avg Commits per committer: 53.8
Development Distribution Score (DDS): 0.089

Past Year

Commits: 2
Committers: 1
Avg Commits per committer: 2.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
VNMabus	v**s@g**m	245
Michael Milton	t**t@g**m	17
jltorrecilla	j****a	4
Felix Laumann	f**n@w**e	2
Ameer Ghouse	a**1@g**m	1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 40
Total pull requests: 28
Average time to close issues: about 2 months
Average time to close pull requests: 10 days
Total issue authors: 24
Total pull request authors: 9
Average comments per issue: 2.78
Average comments per pull request: 1.57
Merged pull requests: 20
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 5
Average time to close issues: N/A
Average time to close pull requests: 5 minutes
Issue authors: 0
Pull request authors: 2
Average comments per issue: 0
Average comments per pull request: 1.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

vnmabus (11)
multimeric (4)
Palash123-4 (4)
amjass12 (2)
asemic-horizon (1)
satra (1)
ninamiolane (1)
zihaozhu93 (1)
DominicHJ45 (1)
alexge233 (1)
srujan741 (1)
jmrichardson (1)
ZebinYang (1)
mycarta (1)
CompRhys (1)

Pull Request Authors

vnmabus (16)
JamesParrott (4)
multimeric (3)
felix-laumann (2)
tr11-sanger (2)
jltorrecilla (1)
lemiceterieux (1)
Palash123-4 (1)
CompRhys (1)

Top Labels

Issue Labels

enhancement (7) help wanted (4) good first issue (4) bug (2)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- pypi 49,487 last-month
Total docker downloads: 77

Total dependent packages: 19
(may contain duplicates)
Total dependent repositories: 36
(may contain duplicates)
Total versions: 23
Total maintainers: 1

pypi.org: dcor

dcor: distance correlation and energy statistics in Python.

Documentation: https://dcor.readthedocs.io/
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.
Latest release: 0.5.7
published over 3 years ago

Versions: 17
Dependent Packages: 18
Dependent Repositories: 36
Downloads: 49,487 Last month
Docker Downloads: 77

Rankings

Dependent packages count: 0.8%

Average: 1.7%

Downloads: 1.8%

Dependent repos count: 2.4%

Maintainers (1)

vnmabus

Last synced: 12 months ago

This package offers functions for calculating several E-statistics such as: - Estimator of the energy distance. - Biased and unbiased estimators of distance covariance and distance correlation. - Estimators of the partial distance covariance and partial distance covariance. It also provides tests based on these E-statistics: - Test of homogeneity based on the energy distance. - Test of independence based on distance covariance.

Homepage: https://github.com/vnmabus/dcor
License: MIT
Latest release: 0.5.7
published over 3 years ago

Versions: 6
Dependent Packages: 1
Dependent Repositories: 0

Rankings

Dependent packages count: 28.8%

Stargazers count: 30.3%

Average: 32.3%

Dependent repos count: 34.0%

Forks count: 36.1%

Last synced: 4 months ago

Dependencies

.github/workflows/main.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite
codecov/codecov-action v2 composite

.github/workflows/python-publish.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite
pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite

binder/requirements.txt pypi

jupytext *
sphinx-gallery <=0.7.0

pyproject.toml pypi

joblib *
numba >=0.51
numpy *
scipy *

dcor

Science Score: 67.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.rst

Owner

Citation (CITATION.cff)

GitHub Events

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pypi.org: dcor

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conda-forge.org: dcor

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