# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "ameras" in publications use:' type: software license: MIT title: 'ameras: Analyze Multiple Exposure Realizations in Association Studies' version: 0.4.0.9000 doi: 10.32614/CRAN.package.ameras abstract: 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 ) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 ), extended regression calibration (Little et al. 2023 ), Monte Carlo maximum likelihood (Stayner et al. 2007 ), frequentist model averaging (Kwon et al. 2023 ), and Bayesian model averaging (Kwon et al. 2016 ). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic. authors: - family-names: Roberti given-names: Sander email: sander.roberti@nih.gov orcid: https://orcid.org/0000-0002-6275-7442 - family-names: Wheeler given-names: William email: WheelerB@imsweb.com - family-names: Kwon given-names: Deukwoo email: DKwon@uams.edu orcid: https://orcid.org/0000-0001-5376-5320 repository: https://sanderroberti.r-universe.dev repository-code: https://github.com/sanderroberti/ameras commit: f8309810bbf383e695a6a8a25731b287669c7d5e url: https://ameras.sanderroberti.com date-released: '2026-06-02' contact: - family-names: Roberti given-names: Sander email: sander.roberti@nih.gov orcid: https://orcid.org/0000-0002-6275-7442