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  "Title": "Statistical Combination of Diagnostic Tests",
  "Description": "A system for combining two diagnostic tests using various\napproaches that include statistical and machine-learning-based\nmethodologies. These approaches are divided into four groups:\nlinear combination methods, non-linear combination methods,\nmathematical operators, and machine learning algorithms. See\nthe <https://biotools.erciyes.edu.tr/dtComb/> website for more\ninformation, documentation, and examples.",
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  "URL": "https://github.com/gokmenzararsiz/dtComb",
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  "License": "MIT + file LICENSE",
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  "Author": "Serra Ilayda Yerlitas [aut, ctb], Serra Bersan Gengec [aut,\nctb], Necla Kochan [aut, ctb], Gozde Erturk Zararsiz [aut,\nctb], Selcuk Korkmaz [aut, ctb], Gokmen Zararsiz [aut, ctb,\ncre]",
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    "helper_PCL",
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    "mathComb",
    "mlComb",
    "nonlinComb",
    "plotComb",
    "std.train",
    "transform_math"
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      "title": "Machine learning model table for mlComb() Includes machine learning models used for the mlComb function",
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      ],
      "fields": [
        "Method",
        "Model"
      ],
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      "table": true,
      "tojson": true
    },
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      "title": "Biomarker data from carriers of a rare genetic disorder A data set containing the carriers of a rare genetic disorder for 120 samples.",
      "object": "exampleData2",
      "class": [
        "data.frame"
      ],
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        "m1",
        "m2",
        "m3",
        "m4"
      ],
      "rows": 120,
      "table": true,
      "tojson": true
    },
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      "name": "exampleData3",
      "title": "Simulated data with healthy and diseased individuals A simulation data containing 250 diseased and 250 healthy individuals.",
      "object": "exampleData3",
      "class": [
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      ],
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        "status",
        "marker1",
        "marker2"
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      "table": true,
      "tojson": true
    },
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      "class": [
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        "ddimer",
        "log_leukocyte"
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      "table": true,
      "tojson": true
    }
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    {
      "page": "allMethods",
      "title": "Machine learning model table for mlComb() Includes machine learning models used for the mlComb function",
      "topics": [
        "allMethods"
      ]
    },
    {
      "page": "availableMethods",
      "title": "Available classification/regression methods in 'dtComb'",
      "topics": [
        "availableMethods"
      ]
    },
    {
      "page": "dtComb",
      "title": "dtComb: A Comprehensive R Package for Combining Diagnostic Tests",
      "topics": [
        "dtComb-package",
        "dtComb"
      ]
    },
    {
      "page": "exampleData2",
      "title": "Biomarker data from carriers of a rare genetic disorder A data set containing the carriers of a rare genetic disorder for 120 samples.",
      "topics": [
        "exampleData2"
      ]
    },
    {
      "page": "exampleData3",
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      "topics": [
        "exampleData3"
      ]
    },
    {
      "page": "helper_minimax",
      "title": "Helper function for minimax method.",
      "topics": [
        "helper_minimax"
      ]
    },
    {
      "page": "helper_minmax",
      "title": "Helper function for minmax method.",
      "topics": [
        "helper_minmax"
      ]
    },
    {
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      "title": "Helper function for PCL method.",
      "topics": [
        "helper_PCL"
      ]
    },
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      "page": "helper_PT",
      "title": "Helper function for PT method.",
      "topics": [
        "helper_PT"
      ]
    },
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      "page": "helper_TS",
      "title": "Helper function for TS method.",
      "topics": [
        "helper_TS"
      ]
    },
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      "page": "kappa.accuracy",
      "title": "Calculate Cohen's kappa and accuracy.",
      "topics": [
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      ]
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      "title": "Diagnostic laparotomy dataset A data set containing the results of diagnostic laparotomy procedures for 225 patients.",
      "topics": [
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      "title": "Linear Combination Methods for Diagnostic Test Scores",
      "topics": [
        "linComb"
      ]
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      "page": "mathComb",
      "title": "Combine two diagnostic tests with several mathematical operators and distance measures.",
      "topics": [
        "mathComb"
      ]
    },
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      "page": "mlComb",
      "title": "Combine two diagnostic tests with Machine Learning Algorithms.",
      "topics": [
        "mlComb"
      ]
    },
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      "page": "nonlinComb",
      "title": "Combine two diagnostic tests with several non-linear combination methods.",
      "topics": [
        "nonlinComb"
      ]
    },
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      "page": "plotComb",
      "title": "Plot the combination scores using the training model",
      "topics": [
        "plotComb"
      ]
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    {
      "page": "predict.dtComb",
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      "topics": [
        "predict.dtComb"
      ]
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      "topics": [
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      "r": "4.6.0",
      "os": "wasm",
      "version": "1.0.7",
      "date": "2026-05-22T08:43:38.000Z",
      "commit": "989694d445ea9f8efdc4764cbb69d0497a405b96",
      "fileid": "774003ef280f8d4ee51a5e1e5541ad1cebab0fb135d8d49540da46479c8673b4",
      "status": "success",
      "buildurl": "https://github.com/r-universe/gokmenzararsiz/actions/runs/25847841917"
    }
  ]
}