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: |
1.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
qgcomp, arm, survival, future, future.apply, ggplot2, gridExtra, rootSolve, numDeriv, MASS |
| Suggests: |
knitr, markdown, devtools |
| Published: |
2025-03-12 |
| 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:
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