nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

Abstract

Longitudinal data from factorial experiments frequently arise in various elds of study,\nranging from medicine and biology to public policy and sociology. In most practical situations,\nthe distribution of observed data is unknown and there may exist a number of\natypical measurements and outliers. Hence, use of parametric and semiparametric procedures\nthat impose restrictive distributional assumptions on observed longitudinal samples\nbecomes questionable. This, in turn, has led to a substantial demand for statistical procedures\nthat enable us to accurately and reliably analyze longitudinal measurements in\nfactorial experiments with minimal conditions on available data, and robust nonparametric\nmethodology o ering such a possibility becomes of particular practical importance.\nIn this article, we introduce a new R package nparLD which provides statisticians and\nresearchers from other disciplines an easy and user-friendly access to the most up-todate\nrobust rank-based methods for the analysis of longitudinal data in factorial settings.\nWe illustrate the implemented procedures by case studies from dentistry, biology, and\nmedicine.