This chapter describes S functions for tree-based modeling. Tree-based models provide an alternative to linear and additive models for regression problems and to linear logistic and additive logistic models for classification problems. Tree-based modeling is an exploratory technique for uncovering structure in data. Specifically, the technique is useful for classification and regression problems where one has a set of classification or predictor variables and a single-response variable. Statistical inference for tree-based models is in its infancy and far behind that for logistic and linear regression analyses. This is partly because a particular type of variable selection underlies tree-based. Our approach is not to have a single function for tree-based modeling, but rather a collection of functions, which, together with existing S functions, form a basis for building and assessing this new class of models. Implementation centers around the idea of a tree object. A subtree of a tree object can be selected or deleted in a natural way through subscripting.