qgam: Smooth Additive Quantile Regression Models

Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>. See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> 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.

Version:	1.3.4
Depends:	R (≥ 3.5.0), mgcv (≥ 1.8-28)
Imports:	shiny, plyr, doParallel, parallel, grDevices
Suggests:	knitr, rmarkdown, MASS, RhpcBLASctl, testthat
Published:	2021-11-22
DOI:	10.32614/CRAN.package.qgam
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

Documentation:

Reference manual:	qgam.pdf
Vignettes:	quantile_mgcViz

Downloads:

Package source:	qgam_1.3.4.tar.gz
Windows binaries:	r-devel: qgam_1.3.4.zip, r-release: qgam_1.3.4.zip, r-oldrel: qgam_1.3.4.zip
macOS binaries:	r-release (arm64): qgam_1.3.4.tgz, r-oldrel (arm64): qgam_1.3.4.tgz, r-release (x86_64): qgam_1.3.4.tgz, r-oldrel (x86_64): qgam_1.3.4.tgz
Old sources:	qgam archive

Reverse dependencies:

Reverse depends:	mgcViz
Reverse imports:	abtest, curvir, DHARMa, sjSDM
Reverse suggests:	ctsem, DAAG

Linking:

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