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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "ppforest2" in publications use:'
type: software
license: MIT
title: 'ppforest2: Projection Pursuit Oblique Decision Trees and Random Forests'
version: 0.1.1
abstract: Builds decision trees by splitting on linear combinations of randomly chosen
  variables. Projection pursuit is used to choose a projection of the variables that
  best separates the groups. Using linear combinations of variables to separate groups
  takes the correlation between variables into account, which allows the model to
  outperform a traditional decision tree when the separation between groups occurs
  in combinations of variables. Single trees can be assembled into random forests
  for improved accuracy. Implements projection pursuit classification trees (Lee,
  Cook, Park and Lee (2013) <https://doi.org/10.1214/13-EJS810>) and projection pursuit
  forests (da Silva, Cook and Lee (2021) <https://doi.org/10.1080/10618600.2020.1870480>),
  following the earlier 'PPforest' package.
authors:
- family-names: Vidal
  given-names: Andrés
  email: andres@andresvidal.dev
- family-names: Silva
  given-names: Natalia
  name-particle: da
repository: https://cran.r-universe.dev
commit: 92d5b49c573fe5beb601b264858d6ca0b1b9d155
date-released: '2026-07-10'
contact:
- family-names: Vidal
  given-names: Andrés
  email: andres@andresvidal.dev