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dcor: distance correlation and related E-statistics in Python.

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Tests Documentation Status Coverage Status Pypi version Available in Conda Zenodo DOI

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.


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.


dcor is available in Python 3.5 or above and in Python 2.7, in all operating systems.


The documentation can be found in



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:, 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.

[SRB07] (1,2)

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.

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