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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.18.10 |
rolling source/ R- | qgcomp_2.18.10.tar.gz |
984.4 KiB |
2.18.10 |
latest source/ R- | qgcomp_2.18.10.tar.gz |
984.4 KiB |
2.18.10 |
2026-04-23 source/ R- | qgcomp_2.18.10.tar.gz |
984.4 KiB |
2.18.10 |
2026-04-09 windows/windows R-4.5 | qgcomp_2.18.10.zip |
1.3 MiB |