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  "Title": "Family of Bayesian EMM Algorithm for Item Response Models",
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  "Date": "2023-06-06",
  "Authors@R": "c(person(\"Shaoyang\", \"Guo\", role = c(\"aut\", \"cre\", \"cph\"),\nemail = \"syguo1992@outlook.com\"),\nperson(\"Chanjin\", \"Zheng\", role = \"aut\"),\nperson(\"Justin L\", \"Kern\", role = \"aut\"))",
  "Description": "Applying the family of the Bayesian\nExpectation-Maximization-Maximization (BEMM) algorithm to\nestimate: (1) Three parameter logistic (3PL) model proposed by\nBirnbaum (1968, ISBN:9780201043105); (2) four parameter\nlogistic (4PL) model proposed by Barton & Lord (1981)\n<doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter\nlogistic guessing (1PLG) and (4) one parameter logistic\nability-based guessing (1PLAG) models proposed by San Martín et\nal (2006) <doi:10.1177/0146621605282773>. The BEMM family\nincludes (1) the BEMM algorithm for 3PL model proposed by Guo &\nZheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM\nalgorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG\nmodel proposed by Guo, Wu, Zheng, & Chen (2021)\n<doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL\nmodel proposed by Zheng, Guo, & Kern (2021)\n<doi:10.1177/21582440211052556>; and (5) their maximum\nlikelihood estimation versions proposed by Zheng, Meng, Guo, &\nLiu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian\nmodal estimates and maximum likelihood estimates are available.",
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    "Date": "2026-05-28 10:45:50 UTC",
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  "Author": "Shaoyang Guo [aut, cre, cph], Chanjin Zheng [aut], Justin L\nKern [aut]",
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    {
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