HRM: High-Dimensional Repeated Measures

Methods for testing main and interaction effects in possibly high-dimensional parametric or nonparametric repeated measures in factorial designs. The observations of the subjects are assumed to be multivariate normal if using the parametric test. The nonparametric version tests with regard to nonparametric relative effects (based on pseudo-ranks). It is possible to use up to 2 whole- and 3 subplot factors. See Happ et al. (2017, <doi:10.1080/15598608.2017.1307792>) for details.

Version:	1.3.0
Depends:	R (≥ 4.2.0)
Imports:	ggplot2, matrixcalc, plyr, data.table, doBy, mvtnorm, Rcpp (≥ 0.12.16), pseudorank (≥ 0.3.7)
LinkingTo:	Rcpp
Suggests:	MASS, testthat
Published:	2026-09-11
DOI:	10.32614/CRAN.package.HRM
Author:	Martin Happ [aut, cre], Solomon W. Harrar [aut], Arne C. Bathke [aut]
Maintainer:	Martin Happ <statistics at happ.co.at>
BugReports:	https://github.com/happma/HRM/issues
License:	GPL-2 | GPL-3
URL:	https://github.com/happma/HRM
NeedsCompilation:	yes
Citation:	HRM citation info
Materials:	README, NEWS
CRAN checks:	HRM results

Documentation:

Reference manual:

HRM.html , HRM.pdf

Downloads:

Package source:	HRM_1.3.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: HRM_1.3.0.zip
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): HRM_1.3.0.tgz, r-release (x86_64): HRM_1.3.0.tgz, r-oldrel (x86_64): HRM_1.3.0.tgz
Old sources:	HRM archive

Linking:

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