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R interface to the wdm C++ library, which provides efficient implementations of weighted dependence measures and related independence tests:
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.
install.packages("wdm")# 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")This repo contains wdm as a submodule. For a full clone use
git clone --recurse-submodules <repo-address>library(wdm)x <- rnorm(100)
y <- rpois(100, 1) # all but Hoeffding's D can handle ties
w <- runif(100)
wdm(x, y, method = "kendall") # unweighted
#> [1] -0.03093257
wdm(x, y, method = "kendall", weights = w) # weighted
#> [1] 0.04835766x <- matrix(rnorm(100 * 3), 100, 3)
wdm(x, method = "spearman") # unweighted
#> [,1] [,2] [,3]
#> [1,] 1.00000000 0.2194659 -0.05435344
#> [2,] 0.21946595 1.0000000 0.11401140
#> [3,] -0.05435344 0.1140114 1.00000000
wdm(x, method = "spearman", weights = w) # weighted
#> [,1] [,2] [,3]
#> [1,] 1.0000000 0.2575236 -0.1689466
#> [2,] 0.2575236 1.0000000 0.1197442
#> [3,] -0.1689466 0.1197442 1.0000000x <- rnorm(100)
y <- rpois(100, 1) # all but Hoeffding's D can handle ties
w <- runif(100)
indep_test(x, y, method = "kendall") # unweighted
#> estimate statistic p_value n_eff method alternative
#> 1 0.1278922 1.532215 0.1254693 100 kendall two-sided
indep_test(x, y, method = "kendall", weights = w) # weighted
#> estimate statistic p_value n_eff method alternative
#> 1 0.1704296 1.779486 0.07516007 79.6939 kendall two-sidedThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.