qgam

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 30 days

This package has been downloaded 18,341 times in the last 30 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 813 times.

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Last 365 days

This package has been downloaded 130,152 times in the last 365 days. This is the kind of download count that makes grant committees nod approvingly. A job well done, even the stoic reviewers might be impressed! The day with the most downloads was Apr 02, 2025 with 813 downloads.

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