This chapter discusses the innovations of additional flexible methods for modeling an individual term in an additive model. It focuses on how we fit additive models. A general and efficient algorithm for fitting a generalized additive model consists of a hierarchy of three modules: scatterplot smoothers, backfitting algorithm, and local-scoring algorithm. These three steps are a rather natural and intuitive generalization of the usual linear model algorithms, and that is how they were originally conceived. The algorithm for fitting a gam is exactly analogous to the algorithm for glms. The chapter presents the S functions for fitting and understanding generalized additive models. In some cases, especially for generalized linear or additive models, adding residuals to a plot is unhelpful because they can distort the scale dramatically. Any interesting features in the functions get lost because of a few large residuals, even though they may carry a very small weight.