Party on!

Recursive partitioning methods are amongst the most popular and widely used statistical learning tools for nonparametric regression and classification. Especially random forests, that can deal with large numbers of predictor variables even in the presence of complex interactions, are being applied successfully in many scientific fields (see, e.g., ??, and the references therein for applications in genetics and social sciences). Thus, it is not surprising that there is a variety of recursive partitioning tools available in R (see for an overview).

Carolin Strobl , Torsten Hothorn , Achim Zeileis


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For attribution, please cite this work as

Strobl, et al., "The R Journal: Party on!", The R Journal, 2009

BibTeX citation

  author = {Strobl, Carolin and Hothorn, Torsten and Zeileis, Achim},
  title = {The R Journal: Party on!},
  journal = {The R Journal},
  year = {2009},
  note = {},
  doi = {10.32614/RJ-2009-013},
  volume = {1},
  issue = {2},
  issn = {2073-4859},
  pages = {14-17}