Hothorn, Torsten partykit: a modular toolkit for recursive partytioning in R. (English) Zbl 1351.62005 J. Mach. Learn. Res. 16, 3905-3909 (2015). Summary: 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)–(d) are available in vignettes accompanying the package. Cited in 17 Documents MSC: 62-04 Software, source code, etc. for problems pertaining to statistics Keywords:recursive partitioning; regression trees; classification trees; statistical learning; R Software:party; RWeka; R; CRAN MachineLearning; rpart; evtree; C4.5; partykit PDFBibTeX XMLCite \textit{T. Hothorn}, J. Mach. Learn. Res. 16, 3905--3909 (2015; Zbl 1351.62005) Full Text: Link