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PEAXAI: Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence

Provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine learning classifiers (Kuhn, 2008) <doi: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) <doi: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) <doi:10.21105/joss.00786>, Shapley value explanations (Strumbelj & Kononenko, 2014) <doi: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.

Version: 0.1.0
Depends: R (≥ 3.5)
Imports: Benchmarking, caret, deaR, dplyr, fastshap, iml, PRROC, pROC, rminer, stats
Suggests: ggplot2, knitr, rmarkdown, nnet
Published: 2025-12-02
DOI: 10.32614/CRAN.package.PEAXAI
Author: Ricardo González Moyano ORCID iD [cre, aut], Juan Aparicio ORCID iD [aut], José Luis Zofío ORCID iD [aut], Víctor España ORCID iD [aut]
Maintainer: Ricardo González Moyano <ricardo.gonzalezm at umh.es>
BugReports: https://github.com/rgonzalezmoyano/PEAXAI/issues
License: GPL-3
URL: https://github.com/rgonzalezmoyano/PEAXAI
NeedsCompilation: no
Language: en
CRAN checks: PEAXAI results

Documentation:

Reference manual: PEAXAI.html , PEAXAI.pdf
Vignettes: PEAXAI: Example with Firms (source, R code)

Downloads:

Package source: PEAXAI_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: PEAXAI_0.1.0.zip, r-oldrel: PEAXAI_0.1.0.zip
macOS binaries: r-release (arm64): PEAXAI_0.1.0.tgz, r-oldrel (arm64): PEAXAI_0.1.0.tgz, r-release (x86_64): PEAXAI_0.1.0.tgz, r-oldrel (x86_64): PEAXAI_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.