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# CITATION 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 "evtree" in publications use:'
type: software
license:
- GPL-2.0-only
- GPL-3.0-only
title: 'evtree: Evolutionary Learning of Globally Optimal Trees'
version: 1.0-8
doi: 10.18637/jss.v061.i01
identifiers:
- type: doi
  value: 10.32614/CRAN.package.evtree
abstract: Commonly used classification and regression tree methods like the CART algorithm
  are recursive partitioning methods that build the model in a forward stepwise search.
  Although this approach is known to be an efficient heuristic, the results of recursive
  tree methods are only locally optimal, as splits are chosen to maximize homogeneity
  at the next step only. An alternative way to search over the parameter space of
  trees is to use global optimization methods like evolutionary algorithms. The 'evtree'
  package implements an evolutionary algorithm for learning globally optimal classification
  and regression trees in R. CPU and memory-intensive tasks are fully computed in
  C++ while the 'partykit' package is leveraged to represent the resulting trees in
  R, providing unified infrastructure for summaries, visualizations, and predictions.
authors:
- family-names: Grubinger
  given-names: Thomas
  email: ThomasGrubinger@gmail.com
- family-names: Zeileis
  given-names: Achim
  email: Achim.Zeileis@R-project.org
  orcid: https://orcid.org/0000-0003-0918-3766
- family-names: Pfeiffer
  given-names: Karl-Peter
  email: Karl-Peter.Pfeiffer@i-med.ac.at
preferred-citation:
  type: article
  title: 'evtree: Evolutionary Learning of Globally Optimal Classification and Regression
    Trees in R'
  authors:
  - family-names: Grubinger
    given-names: Thomas
    email: ThomasGrubinger@gmail.com
  - family-names: Zeileis
    given-names: Achim
    email: Achim.Zeileis@R-project.org
    orcid: https://orcid.org/0000-0003-0918-3766
  - family-names: Pfeiffer
    given-names: Karl-Peter
    email: Karl-Peter.Pfeiffer@i-med.ac.at
  journal: Journal of Statistical Software
  year: '2014'
  volume: '61'
  issue: '1'
  doi: 10.18637/jss.v061.i01
  start: '1'
  end: '29'
repository: https://zeileis.r-universe.dev
commit: 575ca9f0bcd61c6c5ff75c8952766d96dadc34a6
date-released: '2019-05-19'
contact:
- family-names: Grubinger
  given-names: Thomas
  email: ThomasGrubinger@gmail.com