Mixed-effects models are a powerful technique to deal with multiple sources of variability in observed responses. Specialized versions of these models are known as repeated measures models, multilevel models, or hierarchical linear models. The lme4 package for R has been a mainstay for fitting these models over the years, providing methods for fitting both linear mixed-effects models (LMMs) and generalized linear mixed-effects models (GLMMs). In particular, it allows for fitting models with random effects for crossed or partially crossed grouping factors such as
subject and item.

This workshop will provide the statistical background on the form of the models and hands-on experience in using lme4 to fit and evaluate LMMs and GLMMs.