Torsten Hothorn, Achim Zeileis.
Year: 2015, Volume: 16, Issue: 118, Pages: 3905−3909
The R package partykit provides a flexible toolkit for learning, representing, summarizing, and visualizing a wide range of tree- structured regression and classification models. The functionality encompasses: (a) basic infrastructure for representing trees (inferred by any algorithm) so that unified
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 trees (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 recursive partitioning (
mob, also originally in party) along with dedicated methods for trees with parametric models in the leaves. Here, a brief overview of the package and its design is given while more detailed discussions of items (a)—(d) are available in vignettes accompanying the package.