Smooth Additive Quantile Regression Models // 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.