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  "Authors@R": "person(given = c(\"Md\", \"S.\"),\nfamily = \"Warasi\",\nrole = c(\"aut\", \"cre\"),\nemail = \"msarker@radford.edu\")",
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  "Description": "Provides an expectation-maximization (EM) algorithm using\nthe approach introduced in Warasi (2023)\n<doi:10.1080/03610918.2021.2009867>. The EM algorithm can be\nused to estimate the prevalence (overall proportion) of a\ndisease and to estimate a binary regression model from among\nthe class of generalized linear models based on group testing\ndata. The estimation framework we consider offers a flexible\nand general approach; i.e., its application is not limited to\nany specific group testing protocol. Consequently, the EM\nalgorithm can model data arising from simple pooling as well as\nadvanced pooling such as hierarchical testing, array testing,\nand quality control pooling. Also, provided are functions that\ncan be used to conduct the Wald tests described in Buse (1982)\n<doi:10.1080/00031305.1982.10482817> and to simulate the group\ntesting data described in Kim et al. (2007)\n<doi:10.1111/j.1541-0420.2007.00817.x>. We offer a function to\ncompute relative efficiency measures, which can be used to\noptimize group testing designs for maximum likelihood\nestimation of disease prevalence, as described in Warasi and\nDas (2024) <doi:10.1007/s13253-024-00646-6>.",
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