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.
