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 <>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.

Version: 2.0.4
Depends: R (≥ 3.0.1)
Imports: ks (≥ 1.8.0), mvtnorm, utils, Rcpp
LinkingTo: Rcpp
Suggests: testthat
Published: 2024-07-07
DOI: 10.32614/CRAN.package.cdcsis
Author: Wenhao Hu, Mian Huang, Wenliang Pan, Xueqin Wang, Canhong Wen, Yuan Tian, Heping Zhang, Jin Zhu
Maintainer: Jin Zhu <zhuj37 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: cdcsis results


Reference manual: cdcsis.pdf


Package source: cdcsis_2.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): cdcsis_2.0.4.tgz, r-oldrel (arm64): cdcsis_2.0.4.tgz, r-release (x86_64): cdcsis_2.0.4.tgz, r-oldrel (x86_64): cdcsis_2.0.4.tgz
Old sources: cdcsis archive

Reverse dependencies:

Reverse imports: causalBatch


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