Package: qgam 2.0.0

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.

Authors:Matteo Fasiolo [aut, cre], Ben Griffiths [aut], Simon N. Wood [ctb], Margaux Zaffran [ctb], Yannig Goude [ctb], Raphael Nedellec [ctb]

qgam_2.0.0.tar.gz
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qgam_2.0.0.tgz(r-4.4-x86_64)qgam_2.0.0.tgz(r-4.4-arm64)qgam_2.0.0.tgz(r-4.3-x86_64)qgam_2.0.0.tgz(r-4.3-arm64)
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qgam.pdf |qgam.html
qgam/json (API)

# Install 'qgam' in R:
install.packages('qgam', repos = c('https://mfasiolo.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mfasiolo/qgam/issues

Datasets:
  • AUDem - Australian electricity demand data
  • UKload - UK electricity load data

On CRAN:

9.84 score 30 stars 14 packages 140 scripts 9.9k downloads 2 mentions 16 exports 39 dependencies

Last updated 2 months agofrom:56a67086b4. Checks:OK: 1 ERROR: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-win-x86_64ERRORNov 05 2024
R-4.5-linux-x86_64ERRORNov 05 2024
R-4.4-win-x86_64ERRORNov 05 2024
R-4.4-mac-x86_64ERRORNov 05 2024
R-4.4-mac-aarch64ERRORNov 05 2024
R-4.3-win-x86_64ERRORNov 05 2024
R-4.3-mac-x86_64ERRORNov 05 2024
R-4.3-mac-aarch64ERRORNov 05 2024

Exports:checkcheck.learncheck.learnFastcheck.qgamcqcheckcqcheckIelfelflsslog1pexpmqgampinLossqdoqgamsigmoidtuneLearntuneLearnFast

Dependencies:base64encbslibcachemclicodetoolscommonmarkcrayondigestdoParallelfastmapfontawesomeforeachfsgluehtmltoolshttpuviteratorsjquerylibjsonlitelaterlatticelifecyclemagrittrMatrixmemoisemgcvmimenlmeplyrpromisesR6rappdirsRcpprlangsassshinysourcetoolswithrxtable

qgam: quantile non-parametric additive models

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Last update: 2021-07-13
Started: 2016-12-29