A0265
Title: skewlmm: An R Package for fitting skewed and heavy-tailed linear mixed models
Authors: Larissa Avila Matos - Campinas State University (Brazil)
Fernanda Schumacher - The Ohio State University (United States)
Victor Hugo Lachos Davila - University of Connecticut (United States) [presenting]
Abstract: 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. The R package skewlmm provides user-friendly tools to fit linear mixed models by considering the scale mixture of the skew-normal class of distributions, and this robust model formulation accounts for a possible within-subject serial dependence by considering some useful dependence structures, such as autoregressive order p (ARp) and damped exponential correlation (DEC). A real example is used to illustrate the methodology and software.
