energy-package: E-statistics: Multivariate Inference via the Energy of Data
In mariarizzo/energy: E-Statistics: Multivariate Inference via the Energy of Data
energy-package R Documentation
E-statistics: Multivariate Inference via the Energy of Data
Description
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(s)
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,
doi: 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, 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. https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-060116-054026
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.
mariarizzo/energy documentation built on Jan. 1, 2023, 9:43 a.m.
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| energy-package | R Documentation |
E-statistics: Multivariate Inference via the Energy of Data
Description
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(s)
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, doi: 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, 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. https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-060116-054026
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.
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