{
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  "Package": "XAItest",
  "Title": "XAItest: Enhancing Feature Discovery with eXplainable AI",
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  "Authors@R": "c(\nperson(\"Ghislain\", \"FIEVET\", , \"ghislain.fievet@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0337-7327\")),\nperson(\"Sébastien\", \"HERGALANT\", role = \"aut\",\nemail=\"sebastien.hergalant@inserm.fr\", comment=c(ORCID = \"0000-0001-8456-7992\")))",
  "Description": "XAItest is an R Package that identifies features using\neXplainable AI (XAI) methods such as SHAP or LIME. This package\nallows users to compare these methods with traditional\nstatistical tests like t-tests, empirical Bayes, and Fisher's\ntest. Additionally, it includes simThresh, a system that\nenables the comparison of feature importance with p-values by\nincorporating calibrated simulated data.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "biocViews": "Software, StatisticalMethod, FeatureExtraction,\nClassification, Regression",
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  "URL": "https://github.com/GhislainFievet/XAItest",
  "BugReports": "https://github.com/GhislainFievet/XAItest/issues",
  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev zlib1g-dev",
  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 13:04:39 UTC",
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    "getFeatImpThresholds",
    "getMetricsTable",
    "mapPvalImportance",
    "modelsOverview",
    "plotModel",
    "setMetricsTable",
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    {
      "page": "addSimThresh",
      "title": "Add \"simThresh\" Simulated Threshold Feature",
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        "addSimThresh"
      ]
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    {
      "page": "genScenario",
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      ]
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      "title": "The getFeatImpThresholds function identifies the minimum level of feature importance required to exceed a specified significance threshold, which is determined by the p-value.",
      "topics": [
        "getFeatImpThresholds"
      ]
    },
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        "getMetricsTable,ObjXAI-method"
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    },
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        "mapPvalImportance"
      ]
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      "topics": [
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        "ObjXAI-class"
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        "setMetricsTable,ObjXAI-method"
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      "title": "The XAI.test function complements t-test and correlation analyses in feature discovery by integrating eXplainable AI techniques such as feature importance, SHAP, LIME, or custom functions. It provides the option of automatic integration of simulated data to facilitate matching significance between p-values and feature importance.",
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