_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-qgcomp 2.18.4
Propagated dependencies: r-tibble@3.2.1 r-survival@3.7-0 r-rootsolve@1.8.2.4 r-pscl@1.5.9 r-numderiv@2016.8-1.1 r-nnet@7.3-19 r-gridextra@2.3 r-ggplot2@3.5.1 r-generics@0.1.3 r-future-apply@1.11.3 r-future@1.34.0 r-arm@1.14-4 r-aer@1.2-14
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/alexpkeil1/qgcomp/
Licenses: GPL 2+
Synopsis: Quantile G-Computation
Description:

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>.

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