{
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  "Package": "CDM",
  "Type": "Package",
  "Title": "Cognitive Diagnosis Modeling",
  "Version": "8.4-9",
  "Date": "2025-10-30 12:39:38",
  "Authors@R": "c(person(given = \"Alexander\",\nfamily = \"Robitzsch\",\nrole = c(\"aut\", \"cre\"),\nemail = \"robitzsch@ipn.uni-kiel.de\"),\nperson(given = \"Thomas\",\nfamily = \"Kiefer\",\nrole = \"aut\"),\nperson(given = c(\"Ann\", \"Cathrice\"),\nfamily = \"George\",\nrole = \"aut\"),\nperson(given = \"Ali\",\nfamily = \"Uenlue\",\nrole = \"aut\"))",
  "Maintainer": "Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>",
  "Description": "Functions for cognitive diagnosis modeling and\nmultidimensional item response modeling for dichotomous and\npolytomous item responses. This package enables the estimation\nof the DINA and DINO model (Junker & Sijtsma, 2001,\n<doi:10.1177/01466210122032064>), the multiple group\n(polytomous) GDINA model (de la Torre, 2011,\n<doi:10.1007/s11336-011-9207-7>), the multiple choice DINA\nmodel (de la Torre, 2009, <doi:10.1177/0146621608320523>), the\ngeneral diagnostic model (GDM; von Davier, 2008,\n<doi:10.1348/000711007X193957>), the structured latent class\nmodel (SLCA; Formann, 1992,\n<doi:10.1080/01621459.1992.10475229>) and regularized latent\nclass analysis (Chen, Li, Liu, & Ying, 2017,\n<doi:10.1007/s11336-016-9545-6>). See George, Robitzsch,\nKiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> or\nRobitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>)\nfor further details on estimation and the package structure.\nFor tutorials on how to use the CDM package see George and\nRobitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as\nRavand and Robitzsch (2015).",
  "LazyLoad": "yes",
  "LazyData": "yes",
  "URL": "https://github.com/alexanderrobitzsch/CDM,\nhttps://sites.google.com/view/alexander-robitzsch/software",
  "License": "GPL (>= 2)",
  "BugReports": "https://github.com/alexanderrobitzsch/CDM/issues?state=open",
  "Repository": "https://alexanderrobitzsch.r-universe.dev",
  "Date/Publication": "2025-10-30 11:52:47 UTC",
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    "User": "root"
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  "Author": "Alexander Robitzsch [aut, cre],\nThomas Kiefer [aut],\nAnn Cathrice George [aut],\nAli Uenlue [aut]",
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        "CDM"
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        "CDM-utilities",
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      "page": "cdm.est.class.accuracy",
      "title": "Classification Reliability in a CDM",
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    {
      "page": "coef",
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        "coef.gdm",
        "coef.mcdina",
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    {
      "page": "Data-sim",
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        "sim qmatrix"
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    {
      "page": "data.cdm",
      "title": "Several Datasets for the 'CDM' Package",
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        "data.cdm01",
        "data.cdm02",
        "data.cdm03",
        "data.cdm04",
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        "data.cdm06",
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        "data.cdm09",
        "data.cdm10"
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    {
      "page": "data.dcm",
      "title": "Dataset from Book 'Diagnostic Measurement' of Rupp, Templin and Henson (2010)",
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        "data.dcm"
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    },
    {
      "page": "data.dtmr",
      "title": "DTMR Fraction Data (Bradshaw et al., 2014)",
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        "data.dtmr"
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      "page": "data.ecpe",
      "title": "Dataset ECPE",
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        "data.ecpe"
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    {
      "page": "data.fraction",
      "title": "Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices",
      "topics": [
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        "data.fraction1",
        "data.fraction2",
        "data.fraction3",
        "data.fraction4",
        "data.fraction5"
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    },
    {
      "page": "data.hr",
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        "data.hr"
      ]
    },
    {
      "page": "data.jang",
      "title": "Dataset Jang (2009)",
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        "data.jang"
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    {
      "page": "data.melab",
      "title": "MELAB Data (Li, 2011)",
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        "data.melab"
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    {
      "page": "data.mg",
      "title": "Large-Scale Dataset with Multiple Groups",
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        "data.mg"
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    },
    {
      "page": "data.pgdina",
      "title": "Dataset for Polytomous GDINA Model",
      "topics": [
        "data.pgdina"
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    },
    {
      "page": "data.pisa00R",
      "title": "PISA 2000 Reading Study (Chen & de la Torre, 2014)",
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        "data.pisa00R.ct"
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    },
    {
      "page": "data.sda6",
      "title": "Dataset SDA6 (Jurich & Bradshaw, 2014)",
      "topics": [
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    },
    {
      "page": "data.Students",
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    },
    {
      "page": "data.timss03.G8.su",
      "title": "TIMSS 2003 Mathematics 8th Grade (Su et al., 2013)",
      "topics": [
        "data.timss03.G8.su"
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    },
    {
      "page": "data.timss07.G4.lee",
      "title": "TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011)",
      "topics": [
        "data.timss07.G4.lee",
        "data.timss07.G4.py",
        "data.timss07.G4.Qdomains"
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    },
    {
      "page": "data.timss11.G4.AUT",
      "title": "TIMSS 2011 Mathematics 4th Grade Austrian Students",
      "topics": [
        "data.timss11.G4.AUT",
        "data.timss11.G4.AUT.part",
        "data.timss11.G4.sa"
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    },
    {
      "page": "deltaMethod",
      "title": "Variance Matrix of a Nonlinear Estimator Using the Delta Method",
      "topics": [
        "deltaMethod"
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    },
    {
      "page": "din",
      "title": "Parameter Estimation for Mixed DINA/DINO Model",
      "concept": [
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        "binary response data"
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        "din",
        "print.din"
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    },
    {
      "page": "din_identifiability",
      "title": "Identifiability Conditions of the DINA Model",
      "topics": [
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        "summary.din_identifiability"
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    },
    {
      "page": "din.deterministic",
      "title": "Deterministic Classification and Joint Maximum Likelihood Estimation of the Mixed DINA/DINO Model",
      "topics": [
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    },
    {
      "page": "din.equivalent.class",
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      "topics": [
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    },
    {
      "page": "din.validate.qmatrix",
      "title": "Q-Matrix Validation (Q-Matrix Modification) for Mixed DINA/DINO Model",
      "topics": [
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    },
    {
      "page": "discrim.index",
      "title": "Discrimination Indices at Item-Attribute, Item and Test Level",
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        "discrim.index.gdina",
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    },
    {
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      "title": "Test-specific and Item-specific Entropy for Latent Class Models",
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    },
    {
      "page": "equivalent.dina",
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      "topics": [
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    {
      "page": "eval_likelihood",
      "title": "Evaluation of Likelihood",
      "topics": [
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    },
    {
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      "title": "Fraction Subtraction Data",
      "topics": [
        "fraction.subtraction.data"
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    },
    {
      "page": "fraction.subtraction.qmatrix",
      "title": "Fraction Subtraction Q-Matrix",
      "topics": [
        "fraction.subtraction.qmatrix"
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    },
    {
      "page": "gdd",
      "title": "Generalized Distance Discriminating Method",
      "topics": [
        "gdd"
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    },
    {
      "page": "gdina",
      "title": "Estimating the Generalized DINA (GDINA) Model",
      "topics": [
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        "plot.gdina",
        "print.gdina",
        "summary.gdina"
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    },
    {
      "page": "gdina.dif",
      "title": "Differential Item Functioning in the GDINA Model",
      "topics": [
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        "summary.gdina.dif"
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    },
    {
      "page": "gdina.wald",
      "title": "Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model",
      "topics": [
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        "summary.gdina.wald"
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    },
    {
      "page": "gdm",
      "title": "General Diagnostic Model",
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        "plot.gdm",
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        "summary.gdm"
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    },
    {
      "page": "ideal.response.pattern",
      "title": "Ideal Response Pattern",
      "topics": [
        "ideal.response.pattern"
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    },
    {
      "page": "IRT.anova",
      "title": "Helper Function for Conducting Likelihood Ratio Tests",
      "topics": [
        "IRT.anova"
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    {
      "page": "IRT.classify",
      "title": "Individual Classification for Fitted Models",
      "topics": [
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    },
    {
      "page": "IRT.compareModels",
      "title": "Comparisons of Several Models",
      "topics": [
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    },
    {
      "page": "IRT.data",
      "title": "S3 Method for Extracting Used Item Response Dataset",
      "topics": [
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        "IRT.data.gdina",
        "IRT.data.gdm",
        "IRT.data.mcdina",
        "IRT.data.reglca",
        "IRT.data.slca"
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    },
    {
      "page": "IRT.expectedCounts",
      "title": "S3 Method for Extracting Expected Counts",
      "topics": [
        "IRT.expectedCounts",
        "IRT.expectedCounts.din",
        "IRT.expectedCounts.gdina",
        "IRT.expectedCounts.gdm",
        "IRT.expectedCounts.mcdina",
        "IRT.expectedCounts.reglca",
        "IRT.expectedCounts.slca"
      ]
    },
    {
      "page": "IRT.factor.scores",
      "title": "S3 Methods for Extracting Factor Scores (Person Classifications)",
      "topics": [
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        "IRT.factor.scores.din",
        "IRT.factor.scores.gdina",
        "IRT.factor.scores.gdm",
        "IRT.factor.scores.mcdina",
        "IRT.factor.scores.slca"
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    },
    {
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      "title": "S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals",
      "topics": [
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        "IRT.frequencies.din",
        "IRT.frequencies.gdina",
        "IRT.frequencies.gdm",
        "IRT.frequencies.mcdina",
        "IRT.frequencies.slca",
        "IRT_frequencies_default",
        "IRT_frequencies_wrapper"
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    },
    {
      "page": "IRT.IC",
      "title": "Information Criteria",
      "topics": [
        "IRT.IC"
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    },
    {
      "page": "IRT.irfprob",
      "title": "S3 Methods for Extracting Item Response Functions",
      "topics": [
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        "IRT.irfprob.din",
        "IRT.irfprob.gdina",
        "IRT.irfprob.gdm",
        "IRT.irfprob.mcdina",
        "IRT.irfprob.reglca",
        "IRT.irfprob.slca"
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    },
    {
      "page": "IRT.irfprobPlot",
      "title": "Plot Item Response Functions",
      "topics": [
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    },
    {
      "page": "IRT.itemfit",
      "title": "S3 Methods for Computing Item Fit",
      "topics": [
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        "IRT.itemfit.din",
        "IRT.itemfit.gdina",
        "IRT.itemfit.gdm",
        "IRT.itemfit.reglca",
        "IRT.itemfit.slca"
      ]
    },
    {
      "page": "IRT.jackknife",
      "title": "Jackknifing an Item Response Model",
      "topics": [
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        "IRT.derivedParameters",
        "IRT.jackknife",
        "IRT.jackknife.gdina",
        "vcov.IRT.jackknife"
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    },
    {
      "page": "IRT.likelihood",
      "title": "S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior",
      "topics": [
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        "IRT.likelihood.din",
        "IRT.likelihood.gdina",
        "IRT.likelihood.gdm",
        "IRT.likelihood.mcdina",
        "IRT.likelihood.reglca",
        "IRT.likelihood.slca",
        "IRT.posterior",
        "IRT.posterior.din",
        "IRT.posterior.gdina",
        "IRT.posterior.gdm",
        "IRT.posterior.mcdina",
        "IRT.posterior.reglca",
        "IRT.posterior.slca"
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    },
    {
      "page": "IRT.marginal_posterior",
      "title": "S3 Method for Computation of Marginal Posterior Distribution",
      "topics": [
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        "IRT.marginal_posterior.din",
        "IRT.marginal_posterior.gdina",
        "IRT.marginal_posterior.mcdina"
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    },
    {
      "page": "IRT.modelfit",
      "title": "S3 Methods for Assessing Model Fit",
      "topics": [
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        "IRT.modelfit.din",
        "IRT.modelfit.gdina",
        "IRT.modelfit.gdm",
        "summary.IRT.modelfit.din",
        "summary.IRT.modelfit.gdina",
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    },
    {
      "page": "IRT.parameterTable",
      "title": "S3 Method for Extracting a Parameter Table",
      "topics": [
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    },
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