Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM
Sarah Friedrich, Frank Konietschke, Markus Pauly · The R Journal · 2019
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.