cdcsis: Conditional Distance Correlation Based Feature Screening and Conditional Independence Inference

Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <doi:10.5705/ss.202014.0117>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.

Version: 2.0.1
Depends: R (≥ 3.0.1)
Imports: ks (≥ 1.8.0), mvtnorm, Rcpp
LinkingTo: Rcpp
Suggests: testthat
Published: 2019-04-16
Author: Wenhao Hu, Mian Huang, Wenliang Pan, Xueqin Wang, Canhong Wen, Yuan Tian, Heping Zhang, Jin Zhu
Maintainer: Jin Zhu <zhuj37 at mail2.sysu.edu.cn>
BugReports: https://github.com/Mamba413/cdcsis/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/Mamba413/cdcsis
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: cdcsis results

Downloads:

Reference manual: cdcsis.pdf
Package source: cdcsis_2.0.1.tar.gz
Windows binaries: r-devel: cdcsis_2.0.1.zip, r-release: cdcsis_2.0.1.zip, r-oldrel: cdcsis_2.0.1.zip
OS X binaries: r-release: cdcsis_2.0.1.tgz, r-oldrel: cdcsis_2.0.1.tgz
Old sources: cdcsis archive

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