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

TL;DR

The paper introduces the R package MANOVA.RM, which provides robust methods for analyzing multivariate and repeated measures data without relying on strict distributional assumptions, suitable for educational and research use.

Contribution

It presents new statistical methods implemented in MANOVA.RM that do not assume multivariate normality or specific covariance structures, enhancing analysis flexibility.

Findings

Provides accurate p-values and confidence intervals for non-normal data

Includes a user-friendly graphical interface for broader accessibility

Demonstrates methods through multiple real-world examples

Abstract

The numerical availability of statistical inference methods for a modern and robust analysis of longitudinal- and multivariate data in factorial experiments is an essential element in research and education. While existing approaches that rely on specific distributional assumptions of the data (multivariate normality and/or characteristic 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 statistical methods for the analysis of repeated measures designs and multivariate data that 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.