{
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  "Package": "HMDA",
  "Type": "Package",
  "Title": "Holistic Multimodel Domain Analysis for Exploratory Machine\nLearning",
  "Version": "0.3.0",
  "Authors@R": "person(\"E. F. Haghish\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"haghish@hotmail.com\"\n)",
  "Description": "Holistic Multimodel Domain Analysis (HMDA) is a robust and\ntransparent framework designed for exploratory machine learning\nresearch, aiming to enhance the process of feature assessment\nand selection. HMDA addresses key limitations of traditional\nmachine learning methods by evaluating the consistency across\nmultiple high-performing models within a fine-tuned modeling\ngrid, thereby improving the interpretability and reliability of\nfeature importance assessments. Specifically, it computes\nWeighted Mean SHapley Additive exPlanations (WMSHAP), which\naggregate feature contributions from multiple models based on\nweighted performance metrics. HMDA also provides confidence\nintervals to demonstrate the stability of these feature\nimportance estimates. This framework is particularly beneficial\nfor analyzing complex, multidimensional datasets common in\nhealth research, supporting reliable exploration of mental\nhealth outcomes such as suicidal ideation, suicide attempts,\nand other psychological conditions. Additionally, HMDA includes\nautomated procedures for feature selection based on WMSHAP\nratios and performs dimension reduction analyses to identify\nunderlying structures among features. For more details see\nHaghish (2025) <doi:10.13140/RG.2.2.32473.63846>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "URL": "http://dx.doi.org/10.13140/RG.2.2.32473.63846,\nhttps://github.com/haghish/HMDA",
  "BugReports": "https://github.com/haghish/HMDA/issues",
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  "Repository": "https://haghish.r-universe.dev",
  "Date/Publication": "2026-03-04 12:48:02 UTC",
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    "hmda.autoEnsemble",
    "hmda.best.models",
    "hmda.compare.shap.plot",
    "hmda.domain",
    "hmda.efa",
    "hmda.feature.selection",
    "hmda.grid",
    "hmda.grid.analysis",
    "hmda.init",
    "hmda.partition",
    "hmda.plot",
    "hmda.plot.metrics",
    "hmda.row.plot",
    "hmda.search.param",
    "hmda.suggest.param",
    "hmda.test",
    "hmda.wmshap",
    "hmda.wmshap.table",
    "suggest_mtries"
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    {
      "page": "best_of_family",
      "title": "Select Best Models by Performance Metrics",
      "topics": [
        "best_of_family"
      ]
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    {
      "page": "check_efa",
      "title": "Check Exploratory Factor Analysis Suitability",
      "topics": [
        "check_efa"
      ]
    },
    {
      "page": "dictionary",
      "title": "Dictionary of Variable Attributes",
      "topics": [
        "dictionary"
      ]
    },
    {
      "page": "hmda.adjust.params",
      "title": "Adjust Hyperparameter Combinations",
      "topics": [
        "hmda.adjust.params"
      ]
    },
    {
      "page": "hmda.autoEnsemble",
      "title": "Build Stacked Ensemble Model Using autoEnsemble R package",
      "topics": [
        "hmda.autoEnsemble"
      ]
    },
    {
      "page": "hmda.best.models",
      "title": "Select Best Models Across All Models in HMDA Grid",
      "topics": [
        "hmda.best.models"
      ]
    },
    {
      "page": "hmda.compare.shap.plot",
      "title": "Compare SHAP plots across selected models",
      "topics": [
        "hmda.compare.shap.plot"
      ]
    },
    {
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      "topics": [
        "hmda.domain"
      ]
    },
    {
      "page": "hmda.efa",
      "title": "Perform Exploratory Factor Analysis with HMDA",
      "topics": [
        "hmda.efa"
      ]
    },
    {
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      "title": "Feature Selection Based on Weighted SHAP Values",
      "topics": [
        "hmda.feature.selection"
      ]
    },
    {
      "page": "hmda.grid",
      "title": "Tune a Cartesian Hyperparameter Grid in HMDA",
      "topics": [
        "hmda.grid"
      ]
    },
    {
      "page": "hmda.grid.analysis",
      "title": "Analyze Hyperparameter Grid Performance",
      "topics": [
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      ]
    },
    {
      "page": "hmda.init",
      "title": "Initialize or Restart H2O Cluster for HMDA Analysis",
      "topics": [
        "hmda.init"
      ]
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      "title": "Partition Data for HMDA Analysis",
      "topics": [
        "hmda.partition"
      ]
    },
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      "title": "Plot WMSHAP contributions",
      "topics": [
        "hmda.plot"
      ]
    },
    {
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      ]
    },
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      "topics": [
        "hmda.search.param"
      ]
    },
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      "topics": [
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        "hmda.test"
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      "topics": [
        "hmda.wmshap"
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