Generalized additive mixed models (GAMMs) are a type of [[regression model]] that combines the flexibility of generalized additive models (GAMs) with the ability to handle random effects. In GAMMs, the response variable is modeled as a sum of smooth functions of the predictor variables and a set of random effects that account for differences between individual observations. This makes GAMMs a powerful tool for modeling complex relationships between variables and accommodating the dependence structure of repeated measures data.

They are widely used to model correlated and clustered responses. For example, the dependence structure of longitudinal [[data]] and of designs with repeated measurements can be captured.