{ "_id": "6a116b7bacfb0bcc41cf4177", "Package": "tirt", "Title": "Testlet Item Response Theory", "Version": "0.3.1", "Authors@R": "c(\nperson(\"Jiawei\", \"Xiong\", email = \"jiawei.xiong@uga.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Cheng\", \"Tang\", role = \"ctb\"),\nperson(\"Qidi\", \"Liu\", role = \"ctb\")\n)", "Description": "Implementation of Testlet and Item Response Theory. A\nlight-version yet comprehensive and streamlined framework for\npsychometric analysis using unidimensional and multidimensional\nItem Response Theory (IRT; Baker & Kim (2004)\n) and Testlet Response Theory (TRT;\nWainer et al., (2007) ). Designed\nfor researchers, this package supports the estimation of item\nand person parameters for a wide variety of models, including\nbinary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter\nLogistic) and polytomous (Partial Credit Model, Generalized\nPartial Credit Model, Graded Response Model) formats. It also\nsupports the estimation of Testlet models (Rasch Testlet,\n2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet,\nBifactor, Partial Credit Model Testlet, Graded Response),\nallowing users to account for local item dependence in bundled\nitems. A key feature is the specialized support for combination\nuse and joint estimation of item response model and testlet\nresponse model in one calibration. Beyond standard estimation\nvia Marginal Maximum Likelihood with Expectation-Maximization\n(EM) or Joint Maximum Likelihood, the package also offers\nBayesian estimation using priors with maximum a posteriori\n(MAP) method for unidimensional item response theory models. It\nalso provides functions for scale linking and equating\n(Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability\nacross mixed-format test forms. It also facilitates\nfixed-parameter calibration, enabling users to estimate person\nabilities with known item parameters or vice versa, which is\nessential for pre-equating studies and item bank maintenance.\nComprehensive data simulation functions are included to\ngenerate synthetic datasets with complex structures, including\nmixed-model blocks and specific testlet effects, aiding in\nmethodological research and study design validation.\nResearchers can try multiple simulation situations.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-23 08:51:28 UTC", "User": "root" }, "Author": "Jiawei Xiong [aut, cre], Cheng Tang [ctb], Qidi Liu [ctb]", "Maintainer": "Jiawei Xiong ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-03-24 08:40:08 UTC", "RemoteUrl": "https://github.com/cran/tirt", "RemoteRef": "HEAD", 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