arm, future, future.apply, ggplot2, grDevices, grid,
gridExtra, pscl, stats, survival

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; <arXiv:1902.04200>
[stat.ME].

arm, future, future.apply, generics, ggplot2, grDevices, grid,
gridExtra, pscl, stats, survival, tibble

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>.
arm, future, future.apply, generics, ggplot2 (>= 3.3.0),
grDevices, grid, gridExtra, pscl, stats, survival, tibble

arm, future, future.apply, generics, ggplot2 (>= 3.3.0),
grDevices, grid, gridExtra, markdown, pscl, stats, survival,
tibble

broom, devtools, knitr, markdown, MASS, mice

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>.