Alexander R. Groos [cre, aut], Christoph Mayer [ctb]
A fully distributed glacier surface mass balance model developed for the simulation of accumulation and ablation processes on debris-free as well as debris-covered glaciers.
R (>= 2.10), methods, raster, sp, udunits2

Lasso Penalization in Large Scale Generalized Linear Array
Models
Efficient design matrix free procedure for Lasso regularized estimation in large scale 3-dimensional generalized linear array models. The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link is currently implemented.

Penalization in Large Scale Generalized Linear Array Models
Efficient design matrix free procedure for penalized estimation
in large scale 2 and 3-dimensional generalized linear array models. Currently
either Lasso or SCAD penalized estimation is possible for the followings models:
The Gaussian model with identity link, the Binomial model with logit link, the
Poisson model with log link and the Gamma model with log link.

Efficient design matrix free procedure for penalized estimation in large scale 2 and 3-dimensional generalized linear array models. Currently either Lasso or SCAD penalized estimation is possible for the followings models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link.

Efficient design matrix free  lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a  component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs.