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ameras  

Analyze Multiple Exposure Realizations in Association Studies
View on CRAN: Click here


Download and install ameras package within the R console
Install from CRAN:
install.packages("ameras")

Install from Github:
library("remotes")
install_github("cran/ameras")

Install by package version:
library("remotes")
install_version("ameras", "0.1.1")



Attach the package and use:
library("ameras")
Maintained by
Sander Roberti
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-29
Latest Update: 2026-03-29
Description:
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.
How to cite:
Sander Roberti (2026). ameras: Analyze Multiple Exposure Realizations in Association Studies. R package version 0.1.1, https://cran.r-project.org/web/packages/ameras. Accessed 04 Jul. 2026.
Previous versions and publish date:
0.1.1 (2026-03-29 18:40), 0.2.0 (2026-04-26 20:00), 0.3.0 (2026-05-07 11:40)
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Complete documentation for ameras
Functions, R codes and Examples using the ameras R package
Full ameras package functions and examples
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