{ "_id": "6ac30257d743750d376d9f57", "Package": "GDINA", "Type": "Package", "Title": "The Generalized DINA Model Framework", "Version": "2.13.2", "Date": "2026-10-04", "Authors@R": "c(person(given = \"Wenchao\",family = \"Ma\", role = c(\"aut\", \"cre\", \"cph\"),email = \"wma@umn.edu\"),\nperson(given = \"Jimmy\", family = \"de la Torre\", role = c(\"aut\",\"cph\")),\nperson(given=\"Samuel\",family = \"Huang\",role = \"ctb\"),\nperson(given=\"Miguel\",family = \"Sorrel\",role = \"ctb\"),\nperson(given=\"Zhehan\",family = \"Jiang\",role = \"ctb\"),\nperson(given=\"Pablo\",family = \"Najera\",role = \"ctb\"))", "Description": "A set of psychometric tools for cognitive diagnosis\nmodeling based on the generalized deterministic inputs, noisy\nand gate (G-DINA) model by de la Torre (2011)\n and its extensions, including\nthe sequential G-DINA model by Ma and de la Torre (2016)\n for polytomous responses, and the\npolytomous G-DINA model by Chen and de la Torre\n for polytomous attributes. Joint\nattribute distribution can be independent, saturated,\nhigher-order, loglinear smoothed or structured. Q-matrix\nvalidation, item and model fit statistics, model comparison at\ntest and item level and differential item functioning can also\nbe conducted. A graphical user interface is also provided. 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