In tnagler/wdm-r: Weighted Dependence Measures

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wdm

R interface to the wdm C++ library, which provides efficient implementations of weighted dependence measures and related independence tests:

• Pearsons's rho
• Spearmans's rho
• Kendall's tau
• Blomqvist's beta
• Hoeffding's D

All measures are computed in O(n log n) time, where n is the number of observations.

For a detailed description of the functionality, see the API documentation.

Installation

• the stable release from CRAN:

install.packages("wdm")

• the development version from GitHub with:

# install.packages("devtools")
install_submodule_git <- function(x, ...) {
  install_dir <- tempfile()
  system(paste("git clone --recursive", shQuote(x), shQuote(install_dir)))
  devtools::install(install_dir, ...)
}
install_submodule_git("https://github.com/tnagler/wdm-r")

Cloning

This repo contains wdm as a submodule. For a full clone use

git clone --recurse-submodules <repo-address>

Examples

library(wdm)

Dependence between two vectors

x <- rnorm(100)
y <- rpois(100, 1)  # all but Hoeffding's D can handle ties
w <- runif(100)
wdm(x, y, method = "kendall")               # unweighted
wdm(x, y, method = "kendall", weights = w)  # weighted

Dependence in a matrix

x <- matrix(rnorm(100 * 3), 100, 3)
wdm(x, method = "spearman")               # unweighted
wdm(x, method = "spearman", weights = w)  # weighted

Independence test

x <- rnorm(100)
y <- rpois(100, 1)  # all but Hoeffding's D can handle ties
w <- runif(100)
indep_test(x, y, method = "kendall")               # unweighted
indep_test(x, y, method = "kendall", weights = w)  # weighted

tnagler/wdm-r documentation built on Jan. 6, 2025, 1:19 p.m.