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Title
HRM: An R Package for Analysing High-dimensional Multi-factor Repeated Measures
AuthorHapp, Martin ; Harrar, Solomon W. ; Bathke, Arne C.
Published in
The R Journal, New York, 2018, Vol. 10, Issue 1, page 534-548
PublishedNew York : The R Foundation, 2018
LanguageEnglish
Document typeJournal Article
ISSN2073-4859
URNurn:nbn:at:at-ubs:3-10098 Persistent Identifier (URN)
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 The work is publicly available
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HRM: An R Package for Analysing High-dimensional Multi-factor Repeated Measures [0.32 mb]
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Abstract (English)

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.

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CC-BY-License (4.0)Creative Commons Attribution 4.0 International License