{ "_id": "6acceb3aa14063b82c3aa62a", "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; 2006\n) 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)", "Encoding": "UTF-8", "NeedsCompilation": "yes", "Biarch": "true", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.1.0", "Packaged": { "Date": "2026-10-12 13:46:12 UTC", "User": "root" }, "Author": "Haijiang Qin [aut, cre, cph] (ORCID:\n), Lei Guo [aut, cph]\n(ORCID: )", "Maintainer": "Haijiang Qin ", "Config/pak/sysreqs": "cmake make", "Repository": "https://haijiangq.r-universe.dev", "Date/Publication": "2026-09-12 17:10:02 UTC", "RemoteUrl": "https://github.com/cran/ForceChoice", "RemoteRef": "HEAD", "RemoteSha": "7b635b5f5c74281991f9e819f76d844670e343aa", "_user": 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"title": "Convert TIRT Forced-Choice Data to Pairwise-Response Matrix", "topics": [ "get.response.from.data.TIRT" ] }, { "page": "good.of.fit", "title": "Goodness-of-Fit Indices for Latent Probability Models", "topics": [ "good.of.fit" ] }, { "page": "nobs", "title": "S3 Methods: nobs", "topics": [ "nobs", "nobs.FCDCM", "nobs.FCGDINA", "nobs.FCGGUM", "nobs.FCMIRT", "nobs.MGGUM", "nobs.MGPCM", "nobs.MIRT", "nobs.TIRT" ] }, { "page": "plot", "title": "S3 Methods: plot", "topics": [ "plot", "plot.FCDCM", "plot.FCGDINA", "plot.FCGGUM", "plot.FCMIRT", "plot.MGGUM", "plot.MGPCM", "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", 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