PEAXAI: Probabilistic Efficiency Analysis Using Explainable ArtificialIntelligence
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 [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:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=PEAXAIto link to this page.