Package: glamlasso 3.0.1

Adam Lund

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.

Authors:Adam Lund

glamlasso_3.0.1.tar.gz
glamlasso_3.0.1.tar.gz(r-4.5-noble)glamlasso_3.0.1.tar.gz(r-4.4-noble)
glamlasso_3.0.1.tgz(r-4.4-emscripten)glamlasso_3.0.1.tgz(r-4.3-emscripten)
glamlasso.pdf |glamlasso.html
glamlasso/json (API)

# Install 'glamlasso' in R:
install.packages('glamlasso', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.48 score 1 packages 8 scripts 201 downloads 4 exports 2 dependencies

Last updated 4 years agofrom:b14319c14f. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-linux-x86_64NOTENov 29 2024

Exports:glamlassoglamlassoRRglamlassoSRH

Dependencies:RcppRcppArmadillo