Linear mixed-effects models (LMMs) are an important class of statistical
models that can be used to analyze correlated data. Such data are
encountered in a variety of fields including biostatistics, public
health, psychometrics, educational measurement, and sociology. This book
aims to support a wide range of uses for the models by applied
researchers in those and other fields by providing state-of-the-art
descriptions of the implementation of LMMs in R. To help readers to get
familiar with the features of the models and the details of carrying
them out in R, the book includes a review of the most important
theoretical concepts of the models. The presentation connects theory,
software and applications. It is built up incrementally, starting with a
summary of the concepts underlying simpler classes of linear models like
the classical regression model, and carrying them forward to LMMs. A
similar step-by-step approach is used to describe the R tools for LMMs.
All the classes of linear models presented in the book are illustrated
using real-life data. The book also introduces several novel R tools for
LMMs, including new class of variance-covariance structure for
random-effects, methods for influence diagnostics and for power
calculations. They are included into an R package that should assist the
readers in applying these and other methods presented in this text.