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The R package HDNRA provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. It implements classical normal-approximation-based procedures, normal-reference tests based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens–Fisher and GLHT settings. Computationally intensive components are implemented in C++ through Rcpp and RcppArmadillo.
Version 2.1.0 contains 24 public testing routines and two real data sets.
Two real data sets in HDNRA:
Seven normal-reference tests for the two-sample problem:
Seven normal-reference tests for the GLHT/GLHTBF problem:
Four normal-approximation-based tests for the two-sample problem:
Six normal-approximation-based and benchmark tests for the GLHT/GLHTBF problem:
Install the CRAN release with:
install.packages("HDNRA")You can also install the development version from GitHub:
# Installing from GitHub requires devtools or remotes.
install.packages("devtools")
# Or:
install.packages("remotes")
# Install the development version from GitHub.
devtools::install_github("nie23wp8738/HDNRA")
# Or:
remotes::install_github("nie23wp8738/HDNRA")library(HDNRA)Package HDNRA comes with two real data sets:
# A COVID-19 transcriptomic data set for the two-sample problem.
?COVID19
data(COVID19)
dim(COVID19)
group1 <- as.matrix(COVID19[c(2:19, 82:87), ]) # healthy controls
group2 <- as.matrix(COVID19[-c(1:19, 82:87), ]) # COVID-19 patients
dim(group1)
dim(group2)
# A corneal topography data set for the GLHT problem.
?corneal
data(corneal)
dim(corneal)
group1 <- as.matrix(corneal[1:43, ]) # normal group
group2 <- as.matrix(corneal[44:57, ]) # unilateral suspect group
group3 <- as.matrix(corneal[58:78, ]) # suspect map group
group4 <- as.matrix(corneal[79:150, ]) # clinical keratoconus group
dim(group1)
dim(group2)
dim(group3)
dim(group4)A simple example using the F-type normal-reference Behrens–Fisher
test ZWZ2023.TSBF.2cNRT() with the COVID19
data set:
data("COVID19")
group1 <- as.matrix(COVID19[c(2:19, 82:87), ])
group2 <- as.matrix(COVID19[-c(1:19, 82:87), ])
ZWZ2023.TSBF.2cNRT(group1, group2)A simple example using the F-type normal-reference GLHTBF test
WZ2026.GLHTBF.2cNRT() with the corneal data
set:
data("corneal")
group1 <- as.matrix(corneal[1:43, ])
group2 <- as.matrix(corneal[44:57, ])
group3 <- as.matrix(corneal[58:78, ])
group4 <- as.matrix(corneal[79:150, ])
Y <- list(group1, group2, group3, group4)
k <- length(Y)
n <- vapply(Y, nrow, integer(1))
p <- ncol(Y[[1]])
# Omnibus one-way contrast against the last group.
G <- cbind(diag(k - 1), rep(-1, k - 1))
WZ2026.GLHTBF.2cNRT(Y, G, n, p)A rank-one heteroscedastic GLHTBF example can be run with
CCXH2024.GLHTBF.2cNRT():
B <- c(-2, 1, 2, -1)
CCXH2024.GLHTBF.2cNRT(Y, B, n, p, alpha = 0.05)The random-integration normal-approximation GLHTBF routine
LHNB2025.GLHTBF.NABT() uses a rank-one coefficient vector
and tuning vectors O and A:
B <- c(-2, 1, 2, -1)
A <- rep(sqrt(5) * p^(-3 / 8), p)
eps <- 2
O <- sqrt(eps * (1 + 2 * (1:p) / (3 * p)))
LHNB2025.GLHTBF.NABT(Y, B, O, A, n, p)Please note that the HDNRA project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.