E-statistics: Multivariate Inference via the Energy of Data

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.

Author

Maria L. Rizzo and Gabor J. Szekely

References

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, 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.