Package: PEAXAI
Title: Probabilistic Efficiency Analysis Using Explainable Artificial
        Intelligence
Version: 1.0.2
Authors@R: 
  c(
    person(given = "Ricardo",
           family = "González Moyano",
           role = c("cre", "aut"),
           email = "ricardo.gonzalezm@umh.es",
           comment = c(ORCID = "0009-0002-8608-5545")),
    person(given = "Juan",
           family = "Aparicio",
           role = "aut",
           comment = c(ORCID = "0000-0002-0867-0004")),
    person(given = "José Luis",
           family = "Zofío",
           role = "aut",
           comment = c(ORCID = "0000-0003-1170-9501")),
    person(given = "Víctor",
           family = "España",
           role = "aut",
           comment = c(ORCID = "0000-0002-1807-6180"))
  )
Description: 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.
License: GPL-3
URL: https://github.com/rgonzalezmoyano/PEAXAI
BugReports: https://github.com/rgonzalezmoyano/PEAXAI/issues
Encoding: UTF-8
Language: en
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Depends: R (>= 3.5)
Imports: Benchmarking, caret, deaR, dplyr, kernelshap, iml, isotone,
        lime, np, PRROC, pROC, rminer, rms, stats
Suggests: ggplot2, knitr, rmarkdown
VignetteBuilder: knitr
LazyData: false
ByteCompile: true
Config/testthat/edition: 3
Config/pak/sysreqs: cmake libfreetype6-dev libglpk-dev libglu1-mesa-dev
        make texlive libicu-dev libpng-dev libuv1-dev libxml2-dev
        libgl1-mesa-dev libssl-dev zlib1g-dev
Repository: https://rgonzalezmoyano.r-universe.dev
Date/Publication: 2026-06-25 08:17:19 UTC
RemoteUrl: https://github.com/rgonzalezmoyano/peaxai
RemoteRef: HEAD
RemoteSha: 1d0c714f1e3f73ea2e728938973d6d0f7db8bf2c
NeedsCompilation: no
Packaged: 2026-06-25 08:44:10 UTC; root
Author: 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>)
Maintainer: Ricardo González Moyano <ricardo.gonzalezm@umh.es>