Package: highDmean 0.1.0

highDmean: Testing Two-Sample Mean in High Dimension

Implements the high-dimensional two-sample test proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>. It also implements the test proposed by Srivastava, Katayama, and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests are particularly suitable to high dimensional data from two populations for which the classical multivariate Hotelling's T-square test fails due to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>, referred to as zwl_test() in this package, provide a reliable and powerful test.

Authors:Huaiyu Zhang, Haiyan Wang

highDmean_0.1.0.tar.gz
highDmean_0.1.0.zip(r-4.7-any)highDmean_0.1.0.zip(r-4.6-any)highDmean_0.1.0.zip(r-4.5-any)
highDmean_0.1.0.tgz(r-4.6-any)highDmean_0.1.0.tgz(r-4.5-any)
highDmean_0.1.0.tar.gz(r-4.7-any)highDmean_0.1.0.tar.gz(r-4.6-any)
highDmean_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
highDmean/json (API)

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

On CRAN:

Conda:

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

1.70 score 2 scripts 318 downloads 6 exports 0 dependencies

Last updated from:4c817fbddb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK91
source / vignettesOK149
linux-releaseOK88
macos-releaseOK132
macos-oldrelOK167
windows-develOK62
windows-releaseOK40
windows-oldrelOK54
wasm-releaseOK64

Exports:buildDatargammashiftSKK_simSKK_testzwl_simzwl_test

Dependencies: