Package: ameras
Title: Analyze Multiple Exposure Realizations in Association Studies
Version: 0.4.0.9000
Authors@R: c(person(given = "Sander",
                      family = "Roberti",
                      role =  c("aut", "cre"),
                      email = "sander.roberti@nih.gov",
                      comment = c(ORCID = "0000-0002-6275-7442")),
              person(given = "William",
                      family = "Wheeler",
                      role = "aut",
                      email = "WheelerB@imsweb.com"),
               person(given = "Deukwoo",
                      family = "Kwon",
                      role = "aut",
                      email = "DKwon@uams.edu",
                      comment = c(ORCID = "0000-0001-5376-5320")),
               person(given = "Ruth",
                      family = "Pfeiffer",
                      role = "ctb",
                      email = "pfeiffer@mail.nih.gov",
                      comment = c(ORCID = "0000-0001-7791-2698")),
                person("NCI", role=c("cph","fnd")))
Depends: R (>= 4.1.0), stats, nimble
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), future, future.apply,
        ggplot2, dplyr, tidyr, scales, patchwork
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.
License: MIT + file LICENSE
Imports: Rcpp (>= 1.0.10), RcppEigen, coda, numDeriv, mvtnorm, methods,
        MCMCvis, tidyselect, lifecycle, graphics, grDevices
LinkingTo: Rcpp, RcppEigen
NeedsCompilation: yes
VignetteBuilder: knitr
Config/testthat/edition: 3
Author: 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]
Maintainer: Sander Roberti <sander.roberti@nih.gov>
URL: https://ameras.sanderroberti.com,
        https://github.com/sanderroberti/ameras
BugReports: https://github.com/sanderroberti/ameras/issues
Config/pak/sysreqs: cmake libglpk-dev make libuv1-dev libxml2-dev
Repository: https://sanderroberti.r-universe.dev
Date/Publication: 2026-06-24 14:40:25 UTC
RemoteUrl: https://github.com/sanderroberti/ameras
RemoteRef: HEAD
RemoteSha: 793842fc4d7e04239b683dbb8bba719d4b94edcd
Packaged: 2026-06-24 16:29:54 UTC; root