| Name | dcor JSON |
| Version |
0.6
JSON |
| download |
| home_page | |
| Summary | dcor: distance correlation and energy statistics in Python. |
| upload_time | 2022-12-26 17:44:50 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| 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. |
| keywords |
distance correlation
distance covariance
energy distance
e-statistic
dependency measure
homogeneity
independence
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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|
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
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"description": "dcor\n====\n\n|tests| |docs| |coverage| |pypi| |conda| |zenodo|\n\ndcor: distance correlation and energy statistics in Python.\n\nE-statistics are functions of distances between statistical observations\nin metric spaces.\n\nDistance covariance and distance correlation are\ndependency measures between random vectors introduced in [SRB07]_ with\na simple E-statistic estimator.\n\nThis package offers functions for calculating several E-statistics\nsuch as:\n\n- Estimator of the energy distance [SR13]_.\n- Biased and unbiased estimators of distance covariance and\n distance correlation [SRB07]_.\n- Estimators of the partial distance covariance and partial\n distance covariance [SR14]_.\n\nIt also provides tests based on these E-statistics:\n\n- Test of homogeneity based on the energy distance.\n- Test of independence based on distance covariance.\n\nInstallation\n============\n\ndcor is on PyPi and can be installed using :code:`pip`:\n\n.. code::\n\n pip install dcor\n \nIt is also available for :code:`conda` using the :code:`conda-forge` channel:\n\n.. code::\n\n conda install -c conda-forge dcor\n \nPrevious versions of the package were in the :code:`vnmabus` channel. This\nchannel will not be updated with new releases, and users are recommended to\nuse the :code:`conda-forge` channel.\n\nRequirements\n------------\n\ndcor is available in Python 3.8 or above in all operating systems.\nThe package dcor depends on the following libraries:\n\n- numpy\n- numba >= 0.51\n- scipy\n- joblib\n\nDocumentation\n=============\nThe documentation can be found in https://dcor.readthedocs.io/en/latest/?badge=latest\n\nReferences\n==========\n\n.. [SR13] G\u00e1bor J. Sz\u00e9kely and Maria L. Rizzo. Energy statistics: a class of\n statistics based on distances. Journal of Statistical Planning and\n Inference, 143(8):1249 \u2013 1272, 2013.\n URL:\n http://www.sciencedirect.com/science/article/pii/S0378375813000633,\n doi:10.1016/j.jspi.2013.03.018.\n.. [SR14] G\u00e1bor J. Sz\u00e9kely and Maria L. Rizzo. Partial distance correlation\n with methods for dissimilarities. The Annals of Statistics,\n 42(6):2382\u20132412, 12 2014.\n doi:10.1214/14-AOS1255.\n.. [SRB07] G\u00e1bor J. Sz\u00e9kely, Maria L. Rizzo, and Nail K. Bakirov. Measuring and\n testing dependence by correlation of distances. The Annals of\n Statistics, 35(6):2769\u20132794, 12 2007.\n doi:10.1214/009053607000000505.\n\n.. |tests| image:: https://github.com/vnmabus/dcor/actions/workflows/main.yml/badge.svg\n :alt: Tests\n :scale: 100%\n :target: https://github.com/vnmabus/dcor/actions/workflows/main.yml\n\n.. |docs| image:: https://readthedocs.org/projects/dcor/badge/?version=latest\n :alt: Documentation Status\n :scale: 100%\n :target: https://dcor.readthedocs.io/en/latest/?badge=latest\n \n.. |coverage| image:: http://codecov.io/github/vnmabus/dcor/coverage.svg?branch=develop\n :alt: Coverage Status\n :scale: 100%\n :target: https://codecov.io/gh/vnmabus/dcor/branch/develop\n \n.. |pypi| image:: https://badge.fury.io/py/dcor.svg\n :alt: Pypi version\n :scale: 100%\n :target: https://pypi.python.org/pypi/dcor/\n \n.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/dcor\n :alt: Available in Conda\n :scale: 100%\n :target: https://anaconda.org/conda-forge/dcor\n \n.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3468124.svg\n :alt: Zenodo DOI\n :scale: 100%\n :target: https://doi.org/10.5281/zenodo.3468124\n",
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