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. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; <arXiv:1902.04200> [stat.ME].
| Version: | 1.0.0 |
| Depends: | ggplot2 (≥ 2.5), grid, gridExtra, R (≥ 3.0), stats (≥ 3.0) |
| Suggests: | knitr (≥ 1.0) |
| Published: | 2019-03-02 |
| 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_1.0.0.tar.gz |
| Windows binaries: | r-devel: qgcomp_1.0.0.zip, r-release: qgcomp_1.0.0.zip, r-oldrel: qgcomp_1.0.0.zip |
| OS X binaries: | r-release: qgcomp_1.0.0.tgz, r-oldrel: qgcomp_1.0.0.tgz |
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