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glamlasso (version 3.0.1)

Penalization in Large Scale Generalized Linear Array Models

Description

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) . 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.

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Version

Install

install.packages('glamlasso')

Monthly Downloads

198

Version

3.0.1

License

GPL-3

Maintainer

Last Published

May 16th, 2021

Functions in glamlasso (3.0.1)

print.glamlasso

Print Function for objects of Class glamlasso
glamlassoS

Penalization in Large Scale Generalized Linear Array Models
objective

Compute objective values
predict.glamlasso

Make Prediction From a glamlasso Object
RH

The Rotated H-transform of a 3d Array by a Matrix
glamlassoRR

Penalized reduced rank regression in a GLAM
glamlasso_internal

Internal glamlasso Functions
glamlasso

Penalization in Large Scale Generalized Linear Array Models