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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 "e2tree" in publications use:'
type: software
license: MIT
title: 'e2tree: Explainable Ensemble Trees'
version: 1.2.0.9000
doi: 10.1007/s00180-022-01312-6
identifiers:
- type: doi
  value: 10.32614/CRAN.package.e2tree
abstract: The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria
  et al. (2024) <https://doi.org/10.1007/s00180-022-01312-6>. It aims to explain and
  interpret decision tree ensemble models using a single tree-like structure. 'e2tree'
  is a new way of explaining an ensemble tree trained through 'randomForest' or 'xgboost'
  packages.
authors:
- family-names: Aria
  given-names: Massimo
  email: aria@unina.it
  orcid: https://orcid.org/0000-0002-8517-9411
- family-names: Gnasso
  given-names: Agostino
  email: agostino.gnasso@unina.it
  orcid: https://orcid.org/0000-0002-8046-3923
preferred-citation:
  type: article
  title: Explainable ensemble trees
  authors:
  - family-names: Massimo
    given-names: Aria
  - family-names: Agostino
    given-names: Gnasso
  - family-names: Carmela
    given-names: Iorio
  - family-names: Giuseppe
    given-names: Pandolfo
  journal: Computational Statistics
  year: '2024'
  doi: 10.1007/s00180-022-01312-6
  volume: '39'
  issue: '1'
  issn: 1613-9658
  start: 3-19
repository: https://massimoaria.r-universe.dev
repository-code: https://github.com/massimoaria/e2tree
commit: 163220441fbe5b61fb406cbbc824e6d481cbe619
url: https://github.com/massimoaria/e2tree
date-released: '2026-05-16'
contact:
- family-names: Aria
  given-names: Massimo
  email: aria@unina.it
  orcid: https://orcid.org/0000-0002-8517-9411
references:
- type: article
  title: Extending Explainable Ensemble Trees to Regression Contexts
  authors:
  - family-names: Massimo
    given-names: Aria
  - family-names: Agostino
    given-names: Gnasso
  - family-names: Carmela
    given-names: Iorio
  - family-names: Marjolein
    given-names: Fokkema
  journal: Applied Stochastic Models in Business and Industry
  year: '2026'
  doi: 10.1002/asmb.70064
  volume: '42'
  issue: '1'
  start: e70064