r-cran-qgam - 1.3.4-1+b1 main

Smooth additive quantile regression models, fitted using the methods of
Fasiolo et al. (2020) . See Fasiolo
at al. (2021) 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.