{ "_id": "6ac24aa1d743750d376d7dad", "Package": "IRTBEMM", "Type": "Package", "Title": "Family of Bayesian EMM Algorithm for Item Response Models", "Version": "1.0.8", "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; (3) one parameter\nlogistic guessing (1PLG) and (4) one parameter logistic\nability-based guessing (1PLAG) models proposed by San Martín et\nal (2006) . The BEMM family\nincludes (1) the BEMM algorithm for 3PL model proposed by Guo &\nZheng (2019) ; (2) the BEMM\nalgorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG\nmodel proposed by Guo, Wu, Zheng, & Chen (2021)\n; (4) the BEMM algorithm for 4PL\nmodel proposed by Zheng, Guo, & Kern (2021)\n; and (5) their maximum\nlikelihood estimation versions proposed by Zheng, Meng, Guo, &\nLiu (2018) . Thus, both Bayesian\nmodal estimates and maximum likelihood estimates are available.", "License": "GPL (>= 2)", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-04 12:44:07 UTC", "User": "root" }, "Encoding": "UTF-8", "Author": "Shaoyang Guo [aut, cre, cph], Chanjin Zheng [aut], Justin L\nKern [aut]", "Maintainer": "Shaoyang Guo ", "Repository": "https://syguo1992.r-universe.dev", "Date/Publication": "2023-06-06 11:20:05 UTC", "RemoteUrl": "https://github.com/cran/IRTBEMM", "RemoteRef": "HEAD", "RemoteSha": "55dd367fc761e074caebb9462fdf15cb7d113538", "_user": "syguo1992", "_type": "src", "_file": "IRTBEMM_1.0.8.tar.gz", "_fileid": "https://r2.ropensci.org/a427a0c37766123e896afff85b9985852e1daa69f422701b1b0a983f71bf1e24", "_filesize": 206612, "_sha256": "a427a0c37766123e896afff85b9985852e1daa69f422701b1b0a983f71bf1e24", "_expires": "2027-01-12T12:46:23.000Z", "_created": "2026-10-04T12:44:07.000Z", "_published": "2026-10-04T12:46:25.759Z", "_jobs": [ { "job": 111438745740, 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