partykit: A Toolkit for Recursive Partytioning

The partykit package provides a flexible toolkit with infrastructure for learning, rep-resenting, summarizing, and visualizing a wide range of tree-structured regression and classification models. The functionality encompasses: (a) Basic infrastructure for repre-senting trees (inferred by any algorithm) so that unified print/plot/predict methods are available. (b) Dedicated methods for trees with constant fits in the leaves (or terminal nodes) along with suitable coercion functions to create such tree models (e.g., by rpart, RWeka, PMML). (c) A reimplementation of conditional inference trees (ctree, originally provided in the party package). (d) An extended reimplementation of model-based recur-sive partitioning (mob, also originally in party) along with dedicated methods for trees with parametric models in the leaves. This vignette gives a brief overview of the package and discusses in detail the generic infrastructure for representing trees (a). Items (b)–(d) are discussed in the remaining vignettes in the package