Package: ameras 0.5.2.9000

Sander Roberti

ameras: 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.

Authors:Sander Roberti [aut, cre], William Wheeler [aut], Deukwoo Kwon [aut], Ruth Pfeiffer [ctb], U.S. National Cancer Institute [cph, fnd]

ameras_0.5.2.9000.tar.gz
ameras_0.5.2.9000.zip(r-4.7-x86_64)ameras_0.5.2.9000.zip(r-4.7-arm64)ameras_0.5.2.9000.zip(r-4.6-x86_64)ameras_0.5.2.9000.zip(r-4.6-arm64)ameras_0.5.2.9000.zip(r-4.5-x86_64)
ameras_0.5.2.9000.tgz(r-4.6-x86_64)ameras_0.5.2.9000.tgz(r-4.6-arm64)ameras_0.5.2.9000.tgz(r-4.5-x86_64)ameras_0.5.2.9000.tgz(r-4.5-arm64)
ameras_0.5.2.9000.tar.gz(r-4.7-arm64)ameras_0.5.2.9000.tar.gz(r-4.7-x86_64)ameras_0.5.2.9000.tar.gz(r-4.6-arm64)ameras_0.5.2.9000.tar.gz(r-4.6-x86_64)
ameras_0.5.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ameras/json (API)

# Install 'ameras' in R:
install.packages('ameras', repos = c('https://sanderroberti.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sanderroberti/ameras/issues

Pkgdown/docs site:https://ameras.sanderroberti.com

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

6.14 score 19 scripts 352 downloads 11 exports 69 dependencies

Last updated from:01b2352bb4. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK192
linux-devel-x86_64OK195
source / vignettesOK294
linux-release-arm64OK172
linux-release-x86_64OK215
macos-release-arm64OK143
macos-release-x86_64OK391
macos-oldrel-arm64OK162
macos-oldrel-x86_64OK266
windows-devel-arm64OK303
windows-devel-x86_64OK242
windows-release-arm64OK330
windows-release-x86_64OK271
windows-oldrel-x86_64OK179
wasm-releaseOK149

Exports:amerasconvergencedose_lrtecdfplotincluded_realizationsRhatsummary_tabletraceplottransform1transform1.invtransform1.jacobian

Dependencies:abindbackportsBHbriocallrcheckmateclicodacolorspacecpp11crayondescdiffobjdistributionalevaluatefarverfsgenericsggplot2gluegridExtragtableigraphinlineisobandjsonlitelabelinglatticelifecycleloomagrittrMatrixmatrixStatsMCMCvismvtnormnimblenumDerivoteloverlappingpillarpkgbuildpkgconfigpkgloadposteriorpracmapraiseprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrprojrootrstanS7scalesStanHeaderstensorAtestthattibbletidyselectutf8vctrsviridisLitewaldowithr

Confidence intervals
Introduction | Regression calibration, extended regression calibration, and Monte Carlo maximum likelihood | Frequentist and Bayesian model averaging
Rendered fromconfidenceintervals.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-04-01

Effect modification
Introduction | Reference-plus-contrast coding | Subgroup-specific coding | Factor modifiers | Confidence intervals | Choosing a coding
Rendered fromeffectmodification.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-07-15

Fitting models and displaying output
Introduction | Model specification | Example data | Linear regression & displaying output | Logistic regression | Poisson regression | Proportional hazards regression | Multinomial logistic regression | Conditional logistic regression
Rendered frommodelfitting.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-04-01

Manual FMA from regression calibration fits
Introduction | Fit realization-specific RC models | Assemble the FMA result | Compare with built-in FMA | Adapting the example to another analysis | Practical notes
Rendered frommanualfma.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-06-12

Parallel FMA with future
Introduction | Choosing a future plan | Controlling chunk size | Example: sequential and parallel FMA | Reproducibility | Practical recommendations | Parallel FMA or manual FMA?
Rendered fromparallelfma.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-06-15

Parameter transformations
Introduction | Exponential transformation using transform1 | Defining a custom transformation
Rendered fromtransformations.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-04-01

Relative risk models
Introduction | Exponential relative risk | Linear excess relative risk | Linear-exponential relative risk | Comparison between models
Rendered fromrelativeriskmodels.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-04-01

Standard analyses with one dose realization
Introduction | Gaussian outcome | Binary outcome | Count outcome | A note on comparison with other software | Moving to multiple realizations
Rendered fromstandardanalysis.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-06-12