G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. Effect measure modification in this method is a way to assess how the effect of the mixture varies by a binary, categorical or continuous variable. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Version: 1.0.2 Depends: R (≥ 3.5.0) Imports: qgcomp, arm, survival, future, future.apply, ggplot2, gridExtra, rootSolve, numDeriv, MASS Suggests: knitr, markdown, devtools Published: 2025-07-22 DOI: 10.32614/CRAN.package.qgcompint Author: Alexander Keil [aut, cre] Maintainer: Alexander Keil <alex.keil at nih.gov> BugReports: https://github.com/alexpkeil1/qgcompint/issues License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: https://github.com/alexpkeil1/qgcompint/ NeedsCompilation: no Language: en-US Materials: README, NEWS CRAN checks: qgcompint results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=qgcompint to link to this page.
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