<b>qgam</b>: Bayesian Nonparametric Quantile Regression Modeling in <i>R</i>

Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by mgcv R package. While GAM based on assumption that response distribution is modeled parametrically, here we discuss more do not entail any parametric assumption. In particular, this article introduces qgam package, an extension of providing fast calibrated for fitting quantile GAMs (QGAMs) in R. QGAMs a smooth version pinball loss Koenker (2005), rather than likelihood function, hence jointly achieving satisfactory accuracy point estimates and coverage corresponding credible intervals requires adopting specialized framework Fasiolo, Wood, Zaffran, Nedellec, Goude (2021b). Here detail how implemented provide examples illustrating package should used practice.