Generalized additive models are a type of statistical model that use nonparametric or semiparametric functions to predict outcomes based on explanatory variables. These models are flexible and can handle non-normal distributions of data. Recent advancements have improved the estimation methods for these models and introduced a mixed effects framework for more complex analyses.