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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 |
Reference manual: | qgam.pdf |
Vignettes: |
quantile_mgcViz |
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 depends: | mgcViz |
Reverse imports: | abtest, curvir, DHARMa |
Reverse suggests: | ctsem, DAAG |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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