Arbeitspapier

partykit: A modular toolkit for recursive partytioning in R

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)

Language
Englisch

Bibliographic citation
Series: Working Papers in Economics and Statistics ; No. 2014-10

Classification
Wirtschaft
Semiparametric and Nonparametric Methods: General
Neural Networks and Related Topics
Econometric Software
Subject
recursive partitioning
regression trees
classification trees
statistical learning
(d) are available in vignettes accompanying the package.

Event
Geistige Schöpfung
(who)
Hothorn, Torsten
Zeileis, Achim
Event
Veröffentlichung
(who)
University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
(where)
Innsbruck
(when)
2014

Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Hothorn, Torsten
  • Zeileis, Achim
  • University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)

Time of origin

  • 2014

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