Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence R package PEAXAI version 1.0.2 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. Software Depends: R (>= 3.5) Imports: Benchmarking, caret, deaR, dplyr, kernelshap, iml, isotone, lime, np, PRROC, pROC, rminer, rms, stats Suggests: ggplot2, knitr, rmarkdown Ricardo González Moyano <ricardo.gonzalezm@umh.es> Comprehensive R Archive Network (CRAN) Ricardo González Moyano [cre, aut] (ORCID: <https://orcid.org/0009-0002-8608-5545>), Juan Aparicio [aut] (ORCID: <https://orcid.org/0000-0002-0867-0004>), José Luis Zofío [aut] (ORCID: <https://orcid.org/0000-0003-1170-9501>), Víctor España [aut] (ORCID: <https://orcid.org/0000-0002-1807-6180>) GPL-3 2026-06-01 application/tgz https://CRAN.R-project.org/package=PEAXAI doi:10.32614/CRAN.package.PEAXAI en