qgcompint: Quantile G-Computation Extensions for Effect Measure Modification

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: 0.6.2
Depends: R (≥ 3.5.0)
Imports: qgcomp, arm, survival, future, future.apply, ggplot2, gridExtra
Suggests: knitr, markdown
Published: 2021-09-20
Author: Alexander Keil [aut, cre]
Maintainer: Alexander Keil <akeil at unc.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: qgcompint results

Downloads:

Reference manual: qgcompint.pdf
Vignettes: The qgcompint package: g-computation with statistical interaction
Package source: qgcompint_0.6.2.tar.gz
Windows binaries: r-devel: qgcompint_0.6.2.zip, r-release: qgcompint_0.6.2.zip, r-oldrel: qgcompint_0.6.2.zip
macOS binaries: r-release (arm64): qgcompint_0.6.2.tgz, r-release (x86_64): qgcompint_0.6.2.tgz, r-oldrel: qgcompint_0.6.2.tgz

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