{
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  "Package": "ForceChoice",
  "Title": "Forced-Choice Modeling Based on Item Response Theory and\nCognitive Diagnostic Models",
  "Version": "1.0.1",
  "Authors@R": "c(person(given = \"Haijiang\", \nfamily = \"Qin\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"haijiang133@outlook.com\",\ncomment = c(ORCID = \"0009-0000-6721-5653\")),\nperson(given = \"Lei\",\nfamily = \"Guo\",\nrole = c(\"aut\", \"cph\"),\nemail = \"happygl1229@swu.edu.cn\",\ncomment = c(ORCID = \"0000-0002-8273-3587\")))",
  "Description": "Fits, simulates, and evaluates forced-choice and\ntraditional item response theory (IRT) models for noncognitive\nassessment. Eight model families are supported, spanning\ndominance (multidimensional IRT (MIRT) 1PL--4PL;\nmultidimensional generalized partial credit model (MGPCM)),\nideal-point unfolding (multidimensional generalized graded\nunfolding model (MGGUM)), and forced-choice designs\n(forced-choice multidimensional IRT (FCMIRT), forced-choice\ngeneralized graded unfolding model (FCGGUM), Thurstonian IRT\n(TIRT), forced-choice diagnostic classification model (FCDCM),\nforced-choice generalized deterministic inputs, noisy \"and\"\ngate model (FCGDINA)) that mitigate response biases such as\nacquiescence and social desirability. Core estimation backends\ninclude full Bayesian inference via Hamiltonian Monte Carlo\n(Stan) and a fast improved stochastic expectation-maximization\n(iStEM) algorithm suitable for large-scale data; FCGDINA also\nprovides a deterministic expectation-maximization (EM)\nestimator. Comprehensive model evaluation uses the\nlimited-information M2 family of goodness-of-fit statistics\n(Maydeu-Olivares and Joe, 2005\n<doi:10.1198/016214504000002069>; 2006\n<doi:10.1007/s11336-005-1295-9>) together with root mean square\nerror of approximation (RMSEA), comparative fit index (CFI),\nTucker-Lewis index (TLI), and standardized root mean square\nresidual (SRMSR).",
  "License": "GPL (>= 3)",
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  "Packaged": {
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  "Author": "Haijiang Qin [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph]\n(ORCID: <https://orcid.org/0000-0002-8273-3587>)",
  "Maintainer": "Haijiang Qin <haijiang133@outlook.com>",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-09-12 17:10:02 UTC",
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  "_created": "2026-10-12T13:45:51.000Z",
  "_published": "2026-10-12T14:13:03.116Z",
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    "get.fit.index",
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    "get.response.from.data",
    "get.response.from.data.FCDCM",
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    "good.of.fit",
    "model.FCGDINA",
    "rotate",
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    "sim.data.FCGGUM",
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    "sim.data.MGGUM",
    "sim.data.MGPCM",
    "sim.data.MIRT",
    "sim.data.TIRT",
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  "_help": [
    {
      "page": "ForceChoice-package",
      "title": "ForceChoice: Forced-Choice Modeling Based on IRT and CDM",
      "topics": [
        "ForceChoice-package",
        "ForceChoice"
      ]
    },
    {
      "page": "coef",
      "title": "S3 Methods: coef",
      "topics": [
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        "coef.FCDCM",
        "coef.FCGDINA",
        "coef.FCGGUM",
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    },
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      "page": "confint",
      "title": "S3 Methods: confint",
      "topics": [
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        "confint.FCDCM",
        "confint.FCGDINA",
        "confint.FCGGUM",
        "confint.FCMIRT",
        "confint.MGGUM",
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        "confint.TIRT"
      ]
    },
    {
      "page": "deviance",
      "title": "S3 Methods: deviance",
      "topics": [
        "deviance",
        "deviance.FCDCM",
        "deviance.FCGDINA",
        "deviance.FCGGUM",
        "deviance.FCMIRT",
        "deviance.MGGUM",
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        "deviance.MIRT",
        "deviance.TIRT"
      ]
    },
    {
      "page": "extract",
      "title": "S3 Methods: extract",
      "topics": [
        "extract",
        "extract.FCDCM",
        "extract.FCGDINA",
        "extract.FCGGUM",
        "extract.FCMIRT",
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        "extract.MGPCM",
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        "extract.TIRT"
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      "page": "fit.FCDCM",
      "title": "Fit the Forced-Choice Diagnostic Classification Model (FCDCM)",
      "topics": [
        "fit.FCDCM"
      ]
    },
    {
      "page": "fit.FCGDINA",
      "title": "Fit the Forced-Choice GDINA Model",
      "topics": [
        "fit.FCGDINA"
      ]
    },
    {
      "page": "fit.FCGGUM",
      "title": "Fit the Forced-Choice Generalized Graded Unfolding Model (FCGGUM)",
      "topics": [
        "fit.FCGGUM"
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    {
      "page": "fit.FCMIRT",
      "title": "Fit the Forced-Choice Multidimensional IRT (FCMIRT) Model",
      "topics": [
        "fit.FCMIRT"
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    },
    {
      "page": "fit.MGGUM",
      "title": "Fit the Multidimensional Generalized Graded Unfolding Model (MGGUM)",
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    },
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      "page": "fit.MGPCM",
      "title": "Fit the Multidimensional Generalized Partial Credit Model (MGPCM)",
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    },
    {
      "page": "fit.MIRT",
      "title": "Fit the Multidimensional Item Response Theory (MIRT) Model",
      "topics": [
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    },
    {
      "page": "fit.TIRT",
      "title": "Fit the Thurstonian Item Response Theory (TIRT) Model",
      "topics": [
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      ]
    },
    {
      "page": "fitted",
      "title": "S3 Methods: fitted",
      "topics": [
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        "fitted.FCDCM",
        "fitted.FCGDINA",
        "fitted.FCGGUM",
        "fitted.FCMIRT",
        "fitted.MGGUM",
        "fitted.MGPCM",
        "fitted.MIRT",
        "fitted.TIRT"
      ]
    },
    {
      "page": "forced_choice_data_conversion",
      "title": "Forced-Choice Data Conversion Formats",
      "topics": [
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      ]
    },
    {
      "page": "get.block.items.from.data",
      "title": "Extract Block Items from Forced-Choice Data",
      "topics": [
        "get.block.items.from.data"
      ]
    },
    {
      "page": "get.block.items.from.data.FCDCM",
      "title": "Extract Block Items from FCDCM Data",
      "topics": [
        "get.block.items.from.data.FCDCM"
      ]
    },
    {
      "page": "get.data.from.response",
      "title": "Convert an Integer Response (Pattern-Index) Matrix to FC Data Strings",
      "topics": [
        "get.data.from.response"
      ]
    },
    {
      "page": "get.data.from.response.FCDCM",
      "title": "Convert an FCDCM Response Matrix to FC Data Strings",
      "topics": [
        "get.data.from.response.FCDCM"
      ]
    },
    {
      "page": "get.data.from.response.TIRT",
      "title": "Convert TIRT Pairwise-Response Matrix to Forced-Choice Data Strings",
      "topics": [
        "get.data.from.response.TIRT"
      ]
    },
    {
      "page": "get.fit.index",
      "title": "Goodness-of-Fit Indices for Fitted Models",
      "topics": [
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        "get.fit.index.FCDCM",
        "get.fit.index.FCGDINA",
        "get.fit.index.FCGGUM",
        "get.fit.index.FCMIRT",
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    },
    {
      "page": "get.log_lik",
      "title": "Extract pointwise log-likelihood matrix",
      "topics": [
        "get.log_lik",
        "get.log_lik.default"
      ]
    },
    {
      "page": "get.response.from.data",
      "title": "Convert FC Data Strings to an Integer Response (Pattern-Index) Matrix",
      "topics": [
        "get.response.from.data"
      ]
    },
    {
      "page": "get.response.from.data.FCDCM",
      "title": "Convert FCDCM Data Strings to a Binary Response Matrix",
      "topics": [
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      "page": "get.response.from.data.TIRT",
      "title": "Convert TIRT Forced-Choice Data to Pairwise-Response Matrix",
      "topics": [
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      "page": "good.of.fit",
      "title": "Goodness-of-Fit Indices for Latent Probability Models",
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    },
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      "page": "nobs",
      "title": "S3 Methods: nobs",
      "topics": [
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        "nobs.FCDCM",
        "nobs.FCGDINA",
        "nobs.FCGGUM",
        "nobs.FCMIRT",
        "nobs.MGGUM",
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    },
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      "page": "plot",
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      "topics": [
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        "plot.FCDCM",
        "plot.FCGDINA",
        "plot.FCGGUM",
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        "plot.MGGUM",
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        "plot.MIRT",
        "plot.TIRT"
      ]
    },
    {
      "page": "predict",
      "title": "S3 Methods: predict",
      "topics": [
        "predict",
        "predict.FCDCM",
        "predict.FCGDINA",
        "predict.FCGGUM",
        "predict.FCMIRT",
        "predict.MGGUM",
        "predict.MGPCM",
        "predict.MIRT",
        "predict.TIRT"
      ]
    },
    {
      "page": "print",
      "title": "S3 Methods: print",
      "topics": [
        "print",
        "print.data.FCDCM",
        "print.data.FCGDINA",
        "print.data.FCGGUM",
        "print.data.FCMIRT",
        "print.data.MGGUM",
        "print.data.MGPCM",
        "print.data.MIRT",
        "print.data.TIRT",
        "print.FCDCM",
        "print.FCGDINA",
        "print.FCGGUM",
        "print.FCMIRT",
        "print.good.of.fit",
        "print.MGGUM",
        "print.MGPCM",
        "print.MIRT",
        "print.summary.FCDCM",
        "print.summary.FCGDINA",
        "print.summary.FCGGUM",
        "print.summary.FCMIRT",
        "print.summary.good.of.fit",
        "print.summary.MGGUM",
        "print.summary.MGPCM",
        "print.summary.MIRT",
        "print.summary.TIRT",
        "print.TIRT"
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    },
    {
      "page": "residuals",
      "title": "S3 Methods: residuals",
      "topics": [
        "residuals",
        "residuals.FCDCM",
        "residuals.FCGDINA",
        "residuals.FCGGUM",
        "residuals.FCMIRT",
        "residuals.MGGUM",
        "residuals.MGPCM",
        "residuals.MIRT",
        "residuals.TIRT"
      ]
    },
    {
      "page": "rotate",
      "title": "S3 Methods: rotate",
      "topics": [
        "rotate",
        "rotate.MGPCM",
        "rotate.MIRT"
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    },
    {
      "page": "sim.data.FCDCM",
      "title": "Simulate Data from the Forced-Choice DCM Model",
      "topics": [
        "sim.data.FCDCM"
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    },
    {
      "page": "sim.data.FCGDINA",
      "title": "Simulate Forced-Choice GDINA Data",
      "topics": [
        "sim.data.FCGDINA"
      ]
    },
    {
      "page": "sim.data.FCGGUM",
      "title": "Simulate Data from the Forced-Choice GGUM Model",
      "topics": [
        "sim.data.FCGGUM"
      ]
    },
    {
      "page": "sim.data.FCMIRT",
      "title": "Simulate FCMIRT Data",
      "topics": [
        "sim.data.FCMIRT"
      ]
    },
    {
      "page": "sim.data.MGGUM",
      "title": "Simulate Data from the Multidimensional Generalized Graded Unfolding Model",
      "topics": [
        "sim.data.MGGUM"
      ]
    },
    {
      "page": "sim.data.MGPCM",
      "title": "Simulate Data from the Multidimensional Generalized Partial Credit Model",
      "topics": [
        "sim.data.MGPCM"
      ]
    },
    {
      "page": "sim.data.MIRT",
      "title": "Simulate Data from the Multidimensional IRT (MIRT) Model",
      "topics": [
        "sim.data.MIRT"
      ]
    },
    {
      "page": "sim.data.TIRT",
      "title": "Simulate Data from the Thurstonian IRT (TIRT) Model",
      "topics": [
        "sim.data.TIRT"
      ]
    },
    {
      "page": "summary",
      "title": "S3 Methods: summary",
      "topics": [
        "summary",
        "summary.FCDCM",
        "summary.FCGDINA",
        "summary.FCGGUM",
        "summary.FCMIRT",
        "summary.good.of.fit",
        "summary.MGGUM",
        "summary.MGPCM",
        "summary.MIRT",
        "summary.TIRT"
      ]
    },
    {
      "page": "triplets",
      "title": "Triplets of Pairwise Comparisons",
      "topics": [
        "triplets"
      ]
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