Title: Smooth Additive Quantile Regression Models
Author: Matteo Fasiolo, Simon N. Wood, Yannig Goude, Raphael Nedellec.
Description: Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. 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.
Imports: shiny, plyr, doParallel, parallel, grDevices

person("Simon", "N. Wood", role = c("ctb")),
person("Raphael", "Nedellec", role = c("ctb")))
Description: Smooth additive quantile regression models, fitted using
the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. 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.
Suggests: knitr, MASS, RhpcBLASctl, testthat

person("Margaux", "Zaffran", role = c("ctb")),

Suggests: knitr, rmarkdown, MASS, RhpcBLASctl, testthat

See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from

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.  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].
Depends: ggplot2 (>= 2.5), grid, gridExtra, R (>= 3.0), stats (>= 3.0)