Description: E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing multivariate independence based on distance correlation, partial distance correlation, multivariate goodness-of-fit tests, clustering based on energy distance, testing for multivariate normality, distance components (disco) for non-parametric analysis of structured data, and other energy statistics/methods are implemented.

Maria L. Rizzo and Gabor J. Szekely

G. J. Szekely and M. L. Rizzo (2013). Energy statistics:
A class of statistics based on distances, *Journal of
Statistical Planning and Inference*.

M. L. Rizzo and G. J. Szekely (2016). Energy Distance,
*WIRES Computational Statistics*, Wiley, Volume 8 Issue 1, 27-38.
Available online Dec., 2015, tools:::Rd_expr_doi("10.1002/wics.1375").

G. J. Szekely and M. L. Rizzo (2017). The Energy of Data.
*The Annual Review of Statistics and Its Application*
4:447-79.

G. J. Szekely and M. L. Rizzo (2023). *The Energy of Data and Distance Correlation*. Chapman & Hall/CRC Monographs on Statistics and Applied Probability. ISBN 9781482242744.
https://www.routledge.com/The-Energy-of-Data-and-Distance-Correlation/Szekely-Rizzo/p/book/9781482242744.