glamlasso: Penalization in Large Scale Generalized Linear Array Models

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.

Version:	3.0.1
Imports:	Rcpp (≥ 0.11.2)
LinkingTo:	Rcpp, RcppArmadillo
Published:	2021-05-16
Author:	Adam Lund
Maintainer:	Adam Lund <adam.lund at math.ku.dk>
License:	GPL-3
NeedsCompilation:	yes
CRAN checks:	glamlasso results

Documentation:

Reference manual:

glamlasso.pdf

Downloads:

Package source:	glamlasso_3.0.1.tar.gz
Windows binaries:	r-devel: glamlasso_3.0.1.zip, r-release: glamlasso_3.0.1.zip, r-oldrel: glamlasso_3.0.1.zip
macOS binaries:	r-release (arm64): glamlasso_3.0.1.tgz, r-oldrel (arm64): glamlasso_3.0.1.tgz, r-release (x86_64): glamlasso_3.0.1.tgz, r-oldrel (x86_64): glamlasso_3.0.1.tgz
Old sources:	glamlasso archive

Reverse dependencies:

Reverse imports:

FRESHD

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