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.
- Version1.3.4
- R version≥ 3.5.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- qgam citation info
- Last release11/22/2021
Documentation
Team
Matteo Fasiolo
Yannig Goude
Show author detailsRolesContributorSimon N. Wood
Show author detailsRolesContributorRaphael Nedellec
Show author detailsRolesContributorMargaux Zaffran
Show author detailsRolesContributor
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