| dcorVS-package {dcorVS} | R Documentation |
Variable Selection Algorithms Using the Distance Correlation.
Description
The 'FBED' and 'mmpc' variable selection algorithms have been implemented using the distance correlation.
Details
| Package: | dcorVS |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2023-10-17 |
| License: | GPL-2 |
Maintainers
Michail Tsagris mtsagris@uoc.gr.
Author(s)
Michail Tsagris mtsagris@uoc.gr.
References
Szekely G.J., Rizzo M.L. and Bakirov N.K. (2007). Measuring and Testing Independence by Correlation of Distances. Annals of Statistics, 35(6): 2769–2794.
Szekely G.J. and Rizzo M. L. (2014). Partial distance correlation with methods for dissimilarities. Annals of Statistics, 42(6): 2382–2412.
Huo X. and Szekely G.J. (2016). Fast computing for distance covariance. Technometrics, 58(4): 435–447.
Tsamardinos I., Aliferis C. F. and Statnikov A. (2003). Time and sample efficient discovery of Markov blankets and direct causal relations. In Proceedings of the ninth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (pp. 673–678). ACM.
Brown L. E., Tsamardinos I. and Aliferis C. F. (2004). A novel algorithm for scalable and accurate Bayesian network learning. Medinfo, 711–715.
Borboudakis G. and Tsamardinos I. (2019). Forward-backward selection with early dropping. Journal of Machine Learning Research, 20(8): 1–39.