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.
- Version0.7.0
- R version≥ 3.5.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Languageen-US
- Last release03/22/2022
Documentation
Team
Alexander Keil
MaintainerShow author details
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports7 packages
- Suggests3 packages