gdam: Fast Robust Additive Models using Gamma Divergence
Provides a computationally efficient method (at least at the time of development!) for robust additive modeling based on the gamma divergence, implementing the methodology of Hui, Ding, and Sugasawa (2026) <doi:10.1007/s11222-026-10960-3>. The method assumes an identity link and normally distributed errors, and applies the gamma divergence to the resulting restricted maximum likelihood function so as to obtain a loss function that is less sensitive to outlying responses. Because 'gdam' uses a model fitted via the 'mgcv' package as the starting point for optimization, and leverages many of its existing techniques for estimation and inference, users can take advantage of many of the smoothing options available in the 'mgcv' package for constructing additive models.
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[aut, cre],
Shonosuke Sugasawa