# qgam: Bayesian non-parametric quantile regression modelling in R

arXiv: https://arxiv.org/abs/2007.03303 · HTML: https://mlsanity.com/paper/2007.03303/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [The Debiased Score Test: Hunt-and-test for Semiparametric Hypotheses](https://mlsanity.com/paper/2607.28861/) (2026)
- [SPQR: An R Package for Semi-Parametric Density and Quantile Regression](https://mlsanity.com/paper/2210.14482/) (2022)
- [From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting](https://mlsanity.com/paper/2509.14113/) (2025)
- [Modelling non-stationary extremal dependence through a geometric approach](https://mlsanity.com/paper/2509.22501/) (2025)
- [Root Cause Analysis of Measurement and Mechanistic Anomalies](https://mlsanity.com/paper/2601.23026/) (2026)

## Abstract (arXiv)

Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package. While the GAM methods provided by mgcv are based on the assumption that the response distribution is modelled parametrically, here we discuss more flexible methods that do not entail any parametric assumption. In particular, this article introduces the qgam package, which is an extension of mgcv providing fast calibrated Bayesian methods for fitting quantile GAMs (QGAMs) in R. QGAMs are based on a smooth version of the pinball loss of Koenker (2005), rather than on a likelihood function, hence jointly achieving satisfactory accuracy of the quantile point estimates and coverage of the corresponding credible intervals requires adopting the specialized Bayesian fitting framework of Fasiolo, Wood, Zaffran, Nedellec, and Goude (2020b). Here we detail how this framework is implemented in qgam and we provide examples illustrating how the package should be used in practice.