This R package offers methods for fitting additive quantile regression models based on splines, using the methods described in Fasiolo et al., 2017.

See the vignette for an introduction to the most important functions:

• qgam fits an additive quantile regression model to a single quantile. Very similar to mgcv::gam. It returns an mgcv::gamObject.
• mqgam fits the same additive quantile regression model to several quantiles. It is more efficient that calling qgam several time, especially in terms of memory.
• tuneLearn useful for tuning the learning rate of the Gibbs posterior. It evaluates a calibration loss function on a grid of values provided by the user.
• tuneLearnFast similar to tuneLearn, but here the learning rate is selected by minimizing the calibration loss using Brent method.

• Fasiolo, M., Goude Y., Nedellec R. and Wood, S. N. (2017). Fast calibrated additive quantile regression. URL: https://arxiv.org/abs/1707.03307