glamlasso: Penalization in Large Scale Generalized Linear Array Models

Functions capable of performing efficient design matrix free penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The generic glamlasso() function solves the penalized maximum likelihood estimation (PMLE) problem in a pure generalized linear array model (GLAM) as well as in a GLAM containing a non-tensor component. Currently Lasso or Smoothly Clipped Absolute Deviation (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. Furthermore this package also contains two functions that can be used to fit special cases of GLAMs, see glamlassoRR() and glamlassoS(). The procedure underlying these functions is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>.

Version: 3.0
Imports: Rcpp (≥ 0.11.2)
LinkingTo: Rcpp, RcppArmadillo
Published: 2018-01-19
Author: Adam Lund
Maintainer: Adam Lund <adam.lund at math.ku.dk>
License: GPL-3
NeedsCompilation: yes
CRAN checks: glamlasso results

Downloads:

Reference manual: glamlasso.pdf
Package source: glamlasso_3.0.tar.gz
Windows binaries: r-devel: glamlasso_3.0.zip, r-release: glamlasso_3.0.zip, r-oldrel: glamlasso_3.0.zip
OS X binaries: r-release: glamlasso_3.0.tgz, r-oldrel: not available
Old sources: glamlasso archive

Reverse dependencies:

Reverse depends: dynamo

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