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highDmean  

Testing Two-Sample Mean in High Dimension
View on CRAN: Click here


Download and install highDmean package within the R console
Install from CRAN:
install.packages("highDmean")

Install from Github:
library("remotes")
install_github("cran/highDmean")

Install by package version:
library("remotes")
install_version("highDmean", "0.1.0")



Attach the package and use:
library("highDmean")
Maintained by
Huaiyu Zhang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-06-12
Latest Update: 2020-06-12
Description:
Implements the high-dimensional two-sample test proposed by Zhang (2019) . It also implements the test proposed by Srivastava, Katayama, and Kano (2013) . 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) , referred to as zwl_test() in this package, provide a reliable and powerful test.
How to cite:
Huaiyu Zhang (2020). highDmean: Testing Two-Sample Mean in High Dimension. R package version 0.1.0, https://cran.r-project.org/web/packages/highDmean
Previous versions and publish date:
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Functions, R codes and Examples using the highDmean R package
Some associated functions: GO_example . SKK_sim . SKK_test . buildData . highDmean . rgammashift . zwl_sim . zwl_test . 
Some associated R codes: data.R . functions.R . highDmean.R .  Full highDmean package functions and examples
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