Analyze Multiple Exposure Realizations in Association Studies // 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.