High-dimensional longitudinal data pose a serious challenge for statistical inference as many test statistics cannot be computed for high-dimensional data, or they do not maintain the nominal type-I error rate, or have very low power. Therefore, it is necessary to derive new inference methods capable of dealing with high dimensionality, and to make them available to statistics practitioners. One such method is implemented in the package HRM described in this article. This new method uses a similar approach as the Welch-Satterthwaite t-test approximation and works very well for high-dimensional data as long as the data distribution is not too skewed or heavy-tailed. The package also provides a GUI to offer an easy way to apply the methods.
HRM, ggplot2, data.table, RGtk2, RGtk2Extras, cairoDevice, xtable, longitudinal, MANOVA.RM
Graphics, Finance, HighPerformanceComputing, Phylogenetics, ReproducibleResearch
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For attribution, please cite this work as
Happ, et al., "The R Journal: HRM: An R Package for Analysing High-dimensional Multi-factor Repeated Measures", The R Journal, 2018
BibTeX citation
@article{RJ-2018-032,
author = {Happ, Martin and Harrar, Solomon W. and Bathke, Arne C.},
title = {The R Journal: HRM: An R Package for Analysing High-dimensional Multi-factor Repeated Measures},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-032},
doi = {10.32614/RJ-2018-032},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {534-548}
}