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:
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