R/highDmean.R

In highDmean: Testing Two-Sample Mean in High Dimension

#' highDmean: A package for testing of equal mean for two-sample high dimensional data
#'
#' This package is an implementation of the high-dimensional two-sample test
#' proposed by Zhang and Wang (2020) "Result consistency of high dimensional two-sample tests
#' applied to gene ontology terms with gene sets". It also implements the SKK test proposed by
#' Srivastava, Katayama, and Kano (2013) "A two sample test in high dimensional data."
#' These tests are particularly suitable for 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 and Wang (2020), referred to as
#' zwl_test() in this package, provide a reliable and powerful test.
#'
#'
#' @section highDmean functions:
#' The function \code{zwl_test()} conducts the ZWL and ZWLm test of equal mean for two-sample high dimensional data provided in
#' matrices of dimension \code{n} by \code{p} and \code{m}
#' by \code{p}, which are random samples from two populations. It
#' returns the value of test statistic and p-value under the null hypothesis of equal means.
#' The \code{SKK_test()} performs the SKK test and returns the value of test statistic and p-value.
#' The \code{buildData()} function generates simulated high-dimensional data in the two-population setting
#' with specified sample sizes, numbers of components, covariance structure, etc., and
#' the functions \code{zwl_sim()} and \code{SKK_sim()}
#' return test statistic values and p-values for lists of simulated data sets generated by \code{buildData()}.
#'
#' @docType package
#' @name highDmean
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