Description Author(s) References

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*,
http://dx.doi.org/10.1016/j.jspi.2013.03.018

M. L. Rizzo and G. J. Szekely (2016). Energy Distance,
*WIRES Computational Statistics*, Wiley, Volume 8 Issue 1, 27-38.
Available online Dec., 2015, http://dx.doi.org/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. 10.1146/annurev-statistics-060116-054026

energy documentation built on Aug. 12, 2018, 1:04 a.m.

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