Analyze Multiple Exposure Realizations in Association Studies R package ameras version 0.1.1 Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 <doi:10.1667/RR13729.1>) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 <doi:10.1201/9781420010138>), extended regression calibration (Little et al. 2023 <doi:10.1038/s41598-023-42283-y>), Monte Carlo maximum likelihood (Stayner et al. 2007 <doi:10.1667/RR0677.1>), frequentist model averaging (Kwon et al. 2023 <doi:10.1371/journal.pone.0290498>), and Bayesian model averaging (Kwon et al. 2016 <doi:10.1002/sim.6635>). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic. Software Depends: R (>= 3.5.0), stats, nimble Imports: Rcpp (>= 1.0.10), RcppEigen, coda, numDeriv, MCMCvis, mvtnorm, memoise, methods LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2 Sander Roberti <sander.roberti@nih.gov> Comprehensive R Archive Network (CRAN) Sander Roberti [aut, cre] (ORCID: <https://orcid.org/0000-0002-6275-7442>), William Wheeler [aut], Deukwoo Kwon [aut] (ORCID: <https://orcid.org/0000-0001-5376-5320>), Ruth Pfeiffer [ctb] (ORCID: <https://orcid.org/0000-0001-7791-2698>), NCI [cph, fnd] MIT + file LICENSE (https://CRAN.R-project.org/package=ameras/LICENSE) 2026-03-29 application/tgz https://CRAN.R-project.org/package=ameras doi:10.32614/CRAN.package.ameras