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glamlasso  

Penalization in Large Scale Generalized Linear Array Models
View on CRAN: Click here


Download and install glamlasso package within the R console
Install from CRAN:
install.packages("glamlasso")

Install from Github:
library("remotes")
install_github("cran/glamlasso")

Install by package version:
library("remotes")
install_version("glamlasso", "3.0.1")



Attach the package and use:
library("glamlasso")
Maintained by
Adam Lund
[Scholar Profile | Author Map]
First Published: 2015-09-03
Latest Update: 2021-05-16
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.
How to cite:
Adam Lund (2015). glamlasso: Penalization in Large Scale Generalized Linear Array Models. R package version 3.0.1, https://cran.r-project.org/web/packages/glamlasso. Accessed 05 May. 2025.
Previous versions and publish date:
1.0 (2015-09-03 12:20), 2.0.1 (2016-08-19 17:05), 2.0 (2016-08-03 10:09), 3.0 (2018-01-19 15:48)
Other packages that cited glamlasso R package
View glamlasso citation profile
Other R packages that glamlasso depends, imports, suggests or enhances
Complete documentation for glamlasso
Functions, R codes and Examples using the glamlasso R package
Some associated functions: RH . glamlasso . glamlassoRR . glamlassoS . glamlasso_internal . objective . predict.glamlasso . print.glamlasso . 
Some associated R codes: RcppExports.R . glamlasso.R . glamlassoRR.R . glamlassoS.R . glamlasso_RH.R . glamlasso_internal.R . glamlasso_objective.R . glamlasso_predict.R . glamlasso_print.R .  Full glamlasso package functions and examples
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