Conditional Distance Correlation Based Feature Screening and Conditional Independence Inference

Conditional distance correlation 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.

CDC Statistics

Jin Zhu

You can install the released version of cdcsis from CRAN with:



Feature screening

This is a basic example which shows you how to pick out the important feature from high-dimensional dataset:

num <- 100
p <- 1000
x <- matrix(rnorm(num * p), nrow = num, ncol = p)
z <- rnorm(num)
y <- 3*x[, 1] + 1.5*x[, 2] + 4*z*x[, 5] + rnorm(num)
res <- cdcsis(x, y, z)
head(res[["ix"]], n = 10)

cdcsis function successfully selects the informative variables from 1000 features pool.

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GPL (>= 2)


cdcsis 2.0

  • Provide statistical inference method for conditional dependence
  • Supply user-friendly and flexible feature screening interface for R users
  • More concise output of cdcov and cdcor function

Reference manual

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2.0.3 by Jin Zhu, a year ago

Report a bug at

Browse source code at

Authors: Wenhao Hu , Mian Huang , Wenliang Pan , Xueqin Wang , Canhong Wen , Yuan Tian , Heping Zhang , Jin Zhu

Documentation:   PDF Manual  

GPL (>= 2) license

Imports ks, mvtnorm, utils, Rcpp

Suggests testthat

Linking to Rcpp

See at CRAN