dcorVS: Variable Selection Algorithms Using the Distance Correlation

The 'FBED' and 'mmpc' variable selection algorithms have been implemented using the distance correlation. The references include: Tsamardinos I., Aliferis C. F. and Statnikov A. (2003). "Time and sample efficient discovery of Markovblankets and direct causal relations". In Proceedings of the ninth ACM SIGKDD international Conference. <doi:10.1145/956750.956838>. Borboudakis G. and Tsamardinos I. (2019). "Forward-backward selection with early dropping". Journal of Machine Learning Research, 20(8): 1–39. <doi:10.48550/arXiv.1705.10770>. Huo X. and Szekely G.J. (2016). "Fast computing for distance covariance". Technometrics, 58(4): 435–447. <doi:10.1080/00401706.2015.1054435>.

Version: 1.0
Depends: R (≥ 4.0)
Imports: dcov, Rfast, stats
Published: 2023-10-18
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris at uoc.gr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: dcorVS results

Documentation:

Reference manual: dcorVS.pdf

Downloads:

Package source: dcorVS_1.0.tar.gz
Windows binaries: r-devel: dcorVS_1.0.zip, r-release: dcorVS_1.0.zip, r-oldrel: dcorVS_1.0.zip
macOS binaries: r-release (arm64): dcorVS_1.0.tgz, r-oldrel (arm64): dcorVS_1.0.tgz, r-release (x86_64): dcorVS_1.0.tgz

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