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: |
1.0.0 |
| Depends: |
R (≥ 3.5) |
| Imports: |
Benchmarking, caret, deaR, dplyr, fastshap, iml, PRROC, pROC, rminer, stats, rms, isotone |
| Suggests: |
ggplot2, knitr, rmarkdown, nnet |
| Published: |
2026-01-07 |
| DOI: |
10.32614/CRAN.package.PEAXAI |
| Author: |
Ricardo González Moyano
 [cre, aut],
Juan Aparicio
[aut],
José Luis Zofío
[aut],
Víctor España
[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:
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