CRAN/E | qgam

qgam

Smooth Additive Quantile Regression Models

Installation

About

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.

Citation qgam citation info

Key Metrics

Version 1.3.4
R ≥ 3.5.0
Published 2021-11-22 1054 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks qgam results

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Maintainer

Maintainer

Matteo Fasiolo

Authors

Matteo Fasiolo

aut / cre

Simon N. Wood

ctb

Margaux Zaffran

ctb

Yannig Goude

ctb

Raphael Nedellec

ctb

Material

Reference manual
Package source

Vignettes

quantile_mgcViz

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

qgam archive

Depends

R ≥ 3.5.0
mgcv ≥ 1.8-28

Imports

shiny
plyr
doParallel
parallel
grDevices

Suggests

knitr
rmarkdown
MASS
RhpcBLASctl
testthat

Reverse Depends

mgcViz

Reverse Imports

abtest
DHARMa