{
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  "Date": "2019-11-15",
  "Title": "Nonparametric Methods for Cognitive Diagnosis",
  "Authors@R": "c(person(\"Yi\", \"Zheng\", role = c(\"aut\", \"cre\"), email = \"yi.isabel.zheng@asu.edu\"), person(\"Chia-Yi\", \"Chiu\", role = \"aut\"), person(\"Jeffrey A.\", \"Douglas\", role = \"ctb\", email = \"jeffdoug@illinois.edu\"))",
  "Maintainer": "Yi Zheng <yi.isabel.zheng@asu.edu>",
  "Description": "An array of nonparametric and parametric estimation\nmethods for cognitive diagnostic models, including\nnonparametric classification of examinee attribute profiles,\njoint maximum likelihood estimation (JMLE) of examinee\nattribute profiles and item parameters, and nonparametric\nrefinement of the Q-matrix, as well as conditional maximum\nlikelihood estimation (CMLE) of examinee attribute profiles\ngiven item parameters and CMLE of item parameters given\nexaminee attribute profiles. Currently the nonparametric\nmethods in the package support both conjunctive and disjunctive\nmodels, and the parametric methods in the package support the\nDINA model, the DINO model, the NIDA model, the G-NIDA model,\nand the R-RUM model.",
  "LazyLoad": "yes",
  "LazyData": "yes",
  "License": "LGPL (>= 2.1)",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-26 08:28:54 UTC",
    "User": "root"
  },
  "Author": "Yi Zheng [aut, cre], Chia-Yi Chiu [aut], Jeffrey A. Douglas\n[ctb]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2019-11-15 16:20:07 UTC",
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    "author": "Yi Zheng <yi.isabel.zheng@asu.edu>",
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    "message": "version 1.0-11\n",
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
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    "manual.pdf"
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  "_realowner": "cran",
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  "_releases": [
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      "version": "1.0-0",
      "date": "2012-12-22"
    },
    {
      "version": "1.0-1",
      "date": "2013-02-04"
    },
    {
      "version": "1.0-2",
      "date": "2013-04-04"
    },
    {
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  "_exports": [
    "AlphaMLE",
    "AlphaNP",
    "CDL",
    "CDP",
    "ItemFit",
    "JMLE",
    "ModelFit",
    "ParMLE",
    "Qrefine"
  ],
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      "name": "Data.DINA",
      "title": "Example dataset generated using DINA model",
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      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "Data.DINO",
      "title": "Example dataset generated using DINO model",
      "object": "Data.DINO",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "Data.Qrefine",
      "title": "Example dataset used for the 'Qrefine' function",
      "object": "Data.Qrefine",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "NPCD",
      "title": "NPCD: The R Package for Nonparametric Methods for Cognitive Diagnosis",
      "topics": [
        "NPCD-package"
      ]
    },
    {
      "page": "AlphaMLE",
      "title": "Maximum likelihood estimation of attribute profile",
      "topics": [
        "AlphaMLE"
      ]
    },
    {
      "page": "AlphaNP",
      "title": "Nonparametric estimation of attribute profiles",
      "topics": [
        "AlphaNP"
      ]
    },
    {
      "page": "CDL",
      "title": "Log-likelihood for cognitive diagnostic models",
      "topics": [
        "CDL"
      ]
    },
    {
      "page": "CDP",
      "title": "Probability of correct response for cognitive diagnostic models",
      "topics": [
        "CDP"
      ]
    },
    {
      "page": "Data.DINA",
      "title": "Example dataset generated using DINA model",
      "topics": [
        "Data.DINA"
      ]
    },
    {
      "page": "Data.DINO",
      "title": "Example dataset generated using DINO model",
      "topics": [
        "Data.DINO"
      ]
    },
    {
      "page": "Data.Qrefine",
      "title": "Example dataset used for the 'Qrefine' function",
      "topics": [
        "Data.Qrefine"
      ]
    },
    {
      "page": "ItemFit",
      "title": "Compute item fit statistics for outputs generated by estimation functions in the package",
      "topics": [
        "ItemFit"
      ]
    },
    {
      "page": "JMLE",
      "title": "Joint maximum likelihood estimation of item parameters and examinee attribute profiles",
      "topics": [
        "JMLE"
      ]
    },
    {
      "page": "ModelFit",
      "title": "Compute overall model fit statistics for outputs generated by estimation functions in the package",
      "topics": [
        "ModelFit"
      ]
    },
    {
      "page": "ParMLE",
      "title": "Maximum likelihood estimation of item parameters for cognitive diagnostic models.",
      "topics": [
        "ParMLE"
      ]
    },
    {
      "page": "plot.NPCD",
      "title": "Produce diagnostic plots",
      "topics": [
        "plot.AlphaMLE",
        "plot.AlphaNP",
        "plot.JMLE",
        "plot.Qrefine"
      ]
    },
    {
      "page": "print.NPCD",
      "title": "Print outputs generated from the functions in the package.",
      "topics": [
        "print.AlphaMLE",
        "print.AlphaNP",
        "print.JMLE",
        "print.ParMLE",
        "print.Qrefine"
      ]
    },
    {
      "page": "Qrefine",
      "title": "Refine the Q-matrix by minimizing the residual sum of square (RSS)",
      "topics": [
        "Qrefine"
      ]
    }
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