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partykit: A Modular Toolkit for Recursive Partytioning in R

Author

Listed:
  • Torsten Hothorn

    ()

  • Achim Zeileis

    ()

Abstract

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.

Suggested Citation

  • Torsten Hothorn & Achim Zeileis, 2014. "partykit: A Modular Toolkit for Recursive Partytioning in R," Working Papers 2014-10, Faculty of Economics and Statistics, University of Innsbruck.
  • Handle: RePEc:inn:wpaper:2014-10
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    Cited by:

    1. Fu, Wei & Simonoff, Jeffrey S., 2015. "Unbiased regression trees for longitudinal and clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 53-74.
    2. Thomas Rusch & Achim Zeileis, 2014. "Discussion," International Statistical Review, International Statistical Institute, vol. 82(3), pages 361-367, December.

    More about this item

    Keywords

    recursive partitioning; regression trees; classification trees; statistical learning; R;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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