dcor: distance correlation and related E-statistics in Python.
Project description
dcor
dcor: distance correlation and related E-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 pip
:
pip install dcor
It is also available for conda
using the conda-forge
channel:
conda install -c conda-forge dcor
Previous versions of the package were in the vnmabus
channel. This
channel will not be updated with new releases, and users are recommended to
use the conda-forge
channel.
Requirements
dcor is available in Python 3.5 or above and in Python 2.7, in all operating systems.
Documentation
The documentation can be found in https://dcor.readthedocs.io/en/latest/?badge=latest
References
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.
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.
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