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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 "PEAXAI" in publications use:'
type: software
license: GPL-3.0-only
title: 'PEAXAI: Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence'
version: 1.0.2
doi: 10.32614/CRAN.package.PEAXAI
abstract: Provides a probabilistic framework that integrates Data Envelopment Analysis
  (DEA) (Banker et al., 1984) <https://doi.org/10.1287/mnsc.30.9.1078> with machine
  learning classifiers (Kuhn, 2008) <https://doi.org/10.18637/jss.v028.i05> to estimate
  both the (in)efficiency status and the probability of efficiency for decision-making
  units. The approach trains predictive models on DEA-derived efficiency labels (Charnes
  et al., 1985) <https://doi.org/10.1016/0304-4076(85)90133-2>, enabling explainable
  artificial intelligence (XAI) workflows with global and local interpretability tools,
  including permutation importance (Molnar et al., 2018) <https://doi.org/10.21105/joss.00786>,
  Shapley value explanations (Strumbelj & Kononenko, 2014) <https://doi.org/10.1007/s10115-013-0679-x>,
  and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>.
  The framework also supports probability-threshold peer selection and counterfactual
  improvement recommendations for benchmarking and policy evaluation. The probabilistic
  efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based
  Technical Efficiency Analysis through Machine Learning", in review for publication.
authors:
- family-names: González Moyano
  given-names: Ricardo
  email: ricardo.gonzalezm@umh.es
  orcid: https://orcid.org/0009-0002-8608-5545
- family-names: Aparicio
  given-names: Juan
  orcid: https://orcid.org/0000-0002-0867-0004
- family-names: Zofío
  given-names: José Luis
  orcid: https://orcid.org/0000-0003-1170-9501
- family-names: España
  given-names: Víctor
  orcid: https://orcid.org/0000-0002-1807-6180
repository: https://rgonzalezmoyano.r-universe.dev
repository-code: https://github.com/rgonzalezmoyano/PEAXAI
commit: e53b7116fcd30b623447dd02f2ef1c074aed1a12
url: https://github.com/rgonzalezmoyano/PEAXAI
date-released: '2026-06-01'
contact:
- family-names: González Moyano
  given-names: Ricardo
  email: ricardo.gonzalezm@umh.es
  orcid: https://orcid.org/0009-0002-8608-5545