partykit: A modular toolkit for recursive partytioning in R - Torsten Hothorn; Achim Zeileis (University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), 2014)
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 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 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)
Keywords: C14; C45; C87; 330; recursive partitioning; regression trees; classification trees; statistical learning; R; (d) are available in vignettes accompanying the package.