https://rgonzalezmoyano.r-universe.devRecent package updates in rgonzalezmoyanoR-universehttps://github.com/rgonzalezmoyano.pnghttps://rgonzalezmoyano.r-universe.devThu, 25 Jun 2026 08:17:19 GMTricardo.gonzalezm@umh.es (Ricardo González Moyano)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.https://github.com/r-universe/rgonzalezmoyano/actions/runs/28157762414Thu, 25 Jun 2026 08:17:19 GMTPEAXAI1.0.2failurehttps://rgonzalezmoyano.r-universe.devhttps://github.com/rgonzalezmoyano/peaxai