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.

Last updated 5 months ago

9.55 score 30 stars 15 dependents 133 scripts 9.8k downloads

mgcViz - Visualisations for Generalized Additive Models

Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.

Last updated 6 months ago

9.30 score 76 stars 940 scripts 1.9k downloads

esaddle - Extended Empirical Saddlepoint Density Approximations

Tools for fitting the Extended Empirical Saddlepoint (EES) density of Fasiolo et al. (2018) <doi:10.1214/18-EJS1433>.

Last updated 4 years ago

openblascpp

4.11 score 1 stars 26 scripts 225 downloads