Package: wmwAUC 1.0.0

Marian Grendar

wmwAUC: Test of No Group Discrimination Using the WMW Statistic

Implements a wmwAUC test of H0: AUC = 1/2 for continuous, discrete, or mixed random variables, based on the Wilcoxon-Mann-Whitney (WMW) statistic. The classic WMW test is calibrated under H0: {(F, G): F = G} which does not match the set {(F, G): AUC = 1/2}, implied by the test statistic, and consequently leads to erroneous inferences. wmwAUC is calibrated under the correct null and implements two finite-sample corrected p-value methods: an Exact Unbiased (EU) method and a Bias-Corrected (BC) method, both valid for any tie pattern. Methods are described in M. Grendar (2025) "Wilcoxon-Mann-Whitney Test of No Group Discrimination" <doi:10.48550/arXiv.2511.20308>.

Authors:Marian Grendar [aut, cre]

wmwAUC_1.0.0.tar.gz
wmwAUC_1.0.0.zip(r-4.7-any)wmwAUC_1.0.0.zip(r-4.6-any)wmwAUC_1.0.0.zip(r-4.5-any)
wmwAUC_1.0.0.tgz(r-4.6-any)wmwAUC_1.0.0.tgz(r-4.5-any)
wmwAUC_1.0.0.tar.gz(r-4.7-any)wmwAUC_1.0.0.tar.gz(r-4.6-any)
wmwAUC_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
wmwAUC/json (API)

# Install 'wmwAUC' in R:
install.packages('wmwAUC', repos = c('https://grendar.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/grendar/wmwauc/issues

Datasets:

On CRAN:

Conda:

3.40 score 1 stars 4 scripts 166 downloads 10 exports 0 dependencies

Last updated from:b694cb6e06. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK194
source / vignettesOK238
linux-release-x86_64OK180
macos-release-arm64OK196
macos-oldrel-arm64OK184
windows-develOK105
windows-releaseOK83
windows-oldrelOK74
wasm-releaseOK124

Exports:plot_rocpseudomedian_ciroc_with_ciwmw_pvaluewmw_pvalue_tieswmw_testwmwAUC_pseudomedian_ciwmwAUC_pvalue_BCwmwAUC_pvalue_EUwmwAUC_test

Dependencies: