Smooth additive quantile regression models, fitted using
the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. 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.
| Version: |
1.3.2 |
| Depends: |
R (≥ 3.5.0), mgcv (≥ 1.8-28) |
| Imports: |
shiny, plyr, doParallel, parallel, grDevices |
| Suggests: |
knitr, MASS, RhpcBLASctl, testthat |
| Published: |
2020-02-03 |
| Author: |
Matteo Fasiolo [aut, cre],
Simon N. Wood [ctb],
Margaux Zaffran [ctb],
Yannig Goude [ctb],
Raphael Nedellec [ctb] |
| Maintainer: |
Matteo Fasiolo <matteo.fasiolo at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Citation: |
qgam citation info |
| CRAN checks: |
qgam results |