Package: dcov 0.1.1

Hang Weiqiang

dcov: A Fast Implementation of Distance Covariance

Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) <doi:10.1214/009053607000000505>; Székely and Rizzo (2013) <doi:10.1016/j.jmva.2013.02.012>; Székely and Rizzo (2014) <doi:10.1214/14-AOS1255>; Huo and Székely (2016) <doi:10.1080/00401706.2015.1054435>.

Authors:Hang Weiqiang <[email protected]>

# Install 'dcov' in R:

install.packages('dcov', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

Last updated from:dc25d091ec. Checks:6 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	152
linux-devel-x86_64	OK	137
source / vignettes	OK	178
linux-release-arm64	OK	165
linux-release-x86_64	OK	164
wasm-release	OK	141

Dependencies:Rcpp RcppArmadillo

Help page	Topics
Centering method This method implements the double centering and U-centering during computing distance covariance.	centering centering_from_data
Permutation test of distance correlation and partial distance correlation	dcor.test pdcor.test
Distance correlation T-test It uses the result of U-statistic distance correlation to test independence for high dimensional data	dcor.ttest
Distance covariance	dcor dcov
Fast distance covariance for two bivariate variables	dcor2d dcov2d
Marginal distance covariance This function implements the method of calculating distance covariance between y and each column in x	mdcor mdcov
Projection covariance between two random vectors This function implements the projection correlation in 'Zhu et al.(2017)'	pcov
Partial distance covariance	pdcor pdcov