Skewlmm: an R Package for Fitting Skewed and Heavy-Tailed Linear Mixed Models

Longitudinal data are commonly analyzed using linear mixed models, which, for mathematical convenience, usually assume that both random effect and error follow normal distributions. However, these restrictive assumptions may result in a lack of robustness against departures from the normal distribution and invalid statistical inferences. Schumacher, Lachos, and Matos (2021) developed a flexible extension of linear mixed models considering the scale mixture of skew-normal class of distributions from a frequentist point of view, accommodating skewness and heavy tails, and the robust model formulation accounts for a possible within-subject serial dependence by considering some useful the method proposed by Schumacher et al. (2021) and provides a user-friendly tool to fit robust linear mixed models to longitudinal data, including model-fit tests, residual analyzes, and plot functions to support model selection and evaluation. Two data sets and a synthetic example are analyzed to illustrate the methodology and software implementation.