The R Journal: article published in 2018, volume 10:1

HRM: An R Package for Analysing High-dimensional Multi-factor Repeated Measures
Martin Happ, Solomon W. Harrar and Arne C. Bathke , The R Journal (2018) 10:1, pages 534-548.

Abstract 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.

Received: 2018-03-02; online 2018-05-21
CRAN packages: HRM, ggplot2, data.table, RGtk2, RGtk2Extras, cairoDevice, xtable, longitudinal, MANOVA.RM
CRAN Task Views implied by cited CRAN packages: Graphics, Finance, HighPerformanceComputing, Phylogenetics, ReproducibleResearch

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-032,
  author = {Martin Happ and Solomon W. Harrar and Arne C. Bathke},
  title = {{HRM: An R Package for Analysing High-dimensional Multi-
          factor Repeated Measures}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-032},
  url = {https://doi.org/10.32614/RJ-2018-032},
  pages = {534--548},
  volume = {10},
  number = {1}
}