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.

Version: 0.1.0
Depends: R (≥ 3.1.0)
Imports: stats
Published: 2020-06-12
Author: Huaiyu Zhang, Haiyan Wang
Maintainer: Huaiyu Zhang <huaiyuzhang1988 at gmail.com>
License: GPL-2
NeedsCompilation: no
Materials: README NEWS
CRAN checks: highDmean results

Documentation:

Reference manual: highDmean.pdf

Downloads:

Package source: highDmean_0.1.0.tar.gz
Windows binaries: r-devel: highDmean_0.1.0.zip, r-release: highDmean_0.1.0.zip, r-oldrel: highDmean_0.1.0.zip
macOS binaries: r-release (arm64): highDmean_0.1.0.tgz, r-release (x86_64): highDmean_0.1.0.tgz, r-oldrel: highDmean_0.1.0.tgz

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