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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.

copied from cf-staging / r-qgam
Type Size Name Uploaded Downloads Labels
conda 3.3 MB | win-64/r-qgam-1.3.2-r40hda5aaf8_1.tar.bz2  4 years and 8 months ago 1014 main
conda 3.3 MB | win-64/r-qgam-1.3.2-r36hda5aaf8_1.tar.bz2  4 years and 8 months ago 1016 main
conda 3.3 MB | osx-64/r-qgam-1.3.2-r36h17f1fa6_1.tar.bz2  4 years and 8 months ago 309 main
conda 3.2 MB | osx-64/r-qgam-1.3.2-r40h17f1fa6_1.tar.bz2  4 years and 8 months ago 325 main
conda 3.3 MB | linux-64/r-qgam-1.3.2-r40hcdcec82_1.tar.bz2  4 years and 8 months ago 3231 main
conda 3.2 MB | linux-64/r-qgam-1.3.2-r36hcdcec82_1.tar.bz2  4 years and 8 months ago 3228 main
conda 3.3 MB | win-64/r-qgam-1.3.2-r36hda5aaf8_0.tar.bz2  4 years and 8 months ago 1038 main
conda 3.3 MB | win-64/r-qgam-1.3.2-r35hda5aaf8_0.tar.bz2  4 years and 8 months ago 1039 main
conda 3.3 MB | osx-64/r-qgam-1.3.2-r36h17f1fa6_0.tar.bz2  4 years and 8 months ago 328 main
conda 3.3 MB | osx-64/r-qgam-1.3.2-r35h17f1fa6_0.tar.bz2  4 years and 8 months ago 305 main
conda 3.2 MB | linux-64/r-qgam-1.3.2-r36hcdcec82_0.tar.bz2  4 years and 8 months ago 3071 main
conda 3.3 MB | linux-64/r-qgam-1.3.2-r35hcdcec82_0.tar.bz2  4 years and 8 months ago 3048 main

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