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Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) <doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) <doi:10.1177/21582440211052556>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.
| Version: | 1.0.8 |
| Depends: | R (≥ 3.5.0) |
| Published: | 2023-06-06 |
| DOI: | 10.32614/CRAN.package.IRTBEMM |
| Author: | Shaoyang Guo [aut, cre, cph], Chanjin Zheng [aut], Justin L Kern [aut] |
| Maintainer: | Shaoyang Guo <syguo1992 at outlook.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | IRTBEMM citation info |
| Materials: | README NEWS |
| CRAN checks: | IRTBEMM results |
| Reference manual: | IRTBEMM.pdf |
| Package source: | IRTBEMM_1.0.8.tar.gz |
| Windows binaries: | r-devel: IRTBEMM_1.0.8.zip, r-release: IRTBEMM_1.0.8.zip, r-oldrel: IRTBEMM_1.0.8.zip |
| macOS binaries: | r-release (arm64): IRTBEMM_1.0.8.tgz, r-oldrel (arm64): IRTBEMM_1.0.8.tgz, r-release (x86_64): IRTBEMM_1.0.8.tgz, r-oldrel (x86_64): IRTBEMM_1.0.8.tgz |
| Old sources: | IRTBEMM archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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