Smooth additive quantile regression models, fitted using
the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from
'quantreg', the smoothing parameters are estimated automatically by marginal
loss minimization, while the regression coefficients are estimated using either
PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian
credible intervals of the estimated effects have approximately the correct
coverage. The main function is qgam() which is similar to gam() in 'mgcv', but
fits non-parametric quantile regression models.

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].

ggplot2 (>= 2.5), grDevices, grid, gridExtra, R (>= 3.0),
stats (>= 3.0), 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 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, ggplot2, grDevices, grid,
gridExtra, parallel, stats, survival