{ "_id": "6a1f287fb401979e7342355b", "Package": "ameras", "Title": "Analyze Multiple Exposure Realizations in Association Studies", "Version": "0.4.0.9000", "Authors@R": "c(person(given = \"Sander\",\nfamily = \"Roberti\",\nrole = c(\"aut\", \"cre\"),\nemail = \"sander.roberti@nih.gov\",\ncomment = c(ORCID = \"0000-0002-6275-7442\")),\nperson(given = \"William\",\nfamily = \"Wheeler\",\nrole = \"aut\",\nemail = \"WheelerB@imsweb.com\"),\nperson(given = \"Deukwoo\",\nfamily = \"Kwon\",\nrole = \"aut\",\nemail = \"DKwon@uams.edu\",\ncomment = c(ORCID = \"0000-0001-5376-5320\")),\nperson(given = \"Ruth\",\nfamily = \"Pfeiffer\",\nrole = \"ctb\",\nemail = \"pfeiffer@mail.nih.gov\",\ncomment = c(ORCID = \"0000-0001-7791-2698\")),\nperson(\"NCI\", role=c(\"cph\",\"fnd\")))", "Description": "Analyze association studies with multiple realizations of\na noisy or uncertain exposure. These can be obtained from e.g.\na two-dimensional Monte Carlo dosimetry system (Simon et al\n2015 ) to characterize exposure\nuncertainty. The implemented methods are regression calibration\n(Carroll et al. 2006 ), extended\nregression calibration (Little et al. 2023\n), Monte Carlo maximum\nlikelihood (Stayner et al. 2007 ),\nfrequentist model averaging (Kwon et al. 2023\n), and Bayesian model\naveraging (Kwon et al. 2016 ). 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