<b>nparLD</b>: An<i>R</i>Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

This paper presents a meta-modelling framework for estimating the modeled effects of SAS Macros on several different groups of subjects and shows how these effects can vary widely in different experimental groups.

Abstract

Longitudinal data from factorial experiments frequently arise in various fields of study, ranging from medicine and biology to public policy and sociology. In most practical situations, the distribution of observed data is unknown and there may exist a number of atypical measurements and outliers. Hence, use of parametric and semi-parametric procedures that impose restrictive distributional assumptions on observed longitudinal samples becomes questionable. This, in turn, has led to a substantial demand for statistical procedures that enable us to accurately and reliably analyze longitudinal measurements in factorial experiments with minimal conditions on available data, and robust nonparametric methodology offering such a possibility becomes of particular practical importance. In this article, we introduce a new R package nparLD which provides statisticians and researchers from other disciplines an easy and user-friendly access to the most up-to-date robust rank-based methods for the analysis of longitudinal data in factorial settings. We illustrate the implemented procedures by case studies from dentistry, biology, and medicine.