The R Journal: article published in 2009, volume 1:2

Party on!
Carolin Strobl, Torsten Hothorn and Achim Zeileis , The R Journal (2009) 1:2, pages 14-17.

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 http://CRAN.R-project.org/view=MachineLearning for an overview).

@article{RJ-2009-013,
  author = {Carolin Strobl and Torsten Hothorn and Achim Zeileis},
  title = {{Party on!}},
  year = {2009},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2009-013},
  url = {https://doi.org/10.32614/RJ-2009-013},
  pages = {14--17},
  volume = {1},
  number = {2}
}