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.
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 3.8 or above in all operating systems. The package dcor depends on the following libraries:
- numpy
- numba >= 0.51
- scipy
- joblib
Please, if you find this software useful in your work, reference it citing the following paper:
@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:
@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.
The documentation can be found in https://dcor.readthedocs.io/en/latest/?badge=latest
| [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] | (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. |
