semidist: Measure Dependence Between Categorical and Continuous Variables

Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable. Test of independence and feature screening for classification problems can be implemented via the two dependence measures. For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>; Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>; Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.

Version: 0.1.0
Imports: energy, FNN, furrr, purrr, Rcpp, stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0)
Published: 2023-11-21
Author: Wei Zhong [aut], Zhuoxi Li [aut, cre, cph], Wenwen Guo [aut], Hengjian Cui [aut], Runze Li [aut]
Maintainer: Zhuoxi Li <chainchei at gmail.com>
BugReports: https://github.com/wzhong41/semidist/issues
License: MIT + file LICENSE
URL: https://github.com/wzhong41/semidist
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: semidist results

Documentation:

Reference manual: semidist.pdf

Downloads:

Package source: semidist_0.1.0.tar.gz
Windows binaries: r-devel: semidist_0.1.0.zip, r-release: semidist_0.1.0.zip, r-oldrel: semidist_0.1.0.zip
macOS binaries: r-release (arm64): semidist_0.1.0.tgz, r-oldrel (arm64): semidist_0.1.0.tgz, r-release (x86_64): semidist_0.1.0.tgz

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