Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM

Authors

Sarah Friedrich, University Medical Center Göttingen
Frank Konietschke, Corporate Member of Freie Universität Berlin
Markus Pauly, Technische Universität Dortmund

Date of this Version

12-2019

Document Type

Article

Citation

The R Journal (December 2019) 11(2); Editor: Michael J. Kane

Comments

Copyright 2019, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

Nonparametric statistical inference methods for a modern and robust analysis of longitudinal and multivariate data in factorial experiments are essential for research. While existing approaches that rely on specific distributional assumptions of the data (multivariate normality and/or equal covariance matrices) are implemented in statistical software packages, there is a need for user-friendly software that can be used for the analysis of data that do not fulfill the aforementioned assumptions and provide accurate p value and confidence interval estimates. Therefore, newly developed nonparametric statistical methods based on bootstrap- and permutation-approaches, which neither assume multivariate normality nor specific covariance matrices, have been implemented in the freely available R package MANOVA.RM. The package is equipped with a graphical user interface for plausible applications in academia and other educational purpose. Several motivating examples illustrate the application of the methods.