qgcomp: Quantile G-Computation

G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. This approach estimates a regression line corresponding to the expected change in the outcome (on the link basis) given a simultaneous increase in the quantile-based category for all exposures. Works with continuous, binary, and right-censored time-to-event outcomes. 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: 2.4.0
Depends: R (≥ 3.5.0)
Imports: arm, future, future.apply, generics, ggplot2 (≥ 3.3.0), grDevices, grid, gridExtra, markdown, pscl, stats, survival, tibble
Suggests: broom, devtools, knitr, MASS, mice
Published: 2020-07-01
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: qgcomp results


Reference manual: qgcomp.pdf
Vignettes: The qgcomp package: g-computation on exposure quantiles
Package source: qgcomp_2.4.0.tar.gz
Windows binaries: r-devel: qgcomp_2.3.0.zip, r-release: qgcomp_2.3.0.zip, r-oldrel: qgcomp_2.3.0.zip
macOS binaries: r-release: qgcomp_2.3.0.tgz, r-oldrel: qgcomp_2.3.0.tgz
Old sources: qgcomp archive


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