Package: gglasso 1.4

gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm

A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.

Authors:Yi Yang <[email protected]>, Hui Zou <[email protected]>

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gglasso.pdf |gglasso.html✨
gglasso/json (API)

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

Peer review:

Bug tracker:https://github.com/emeryyi/gglasso/issues

Uses libs:
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • bardet - Simplified gene expression data from Scheetz et al.
  • colon - Simplified gene expression data from Alon et al.

On CRAN:

fortran

8.07 score 10 stars 9 packages 292 scripts 571 downloads 3 mentions 6 exports 0 dependencies

Last updated 5 years agofrom:7d98ba6ffb. Checks:2 OK, 6 WARNING, 1 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 06 2025
R-4.5-win-x86_64WARNINGJan 06 2025
R-4.5-linux-x86_64WARNINGJan 06 2025
R-4.4-win-x86_64NOTEJan 06 2025
R-4.4-mac-x86_64WARNINGJan 06 2025
R-4.4-mac-aarch64WARNINGJan 06 2025
R-4.3-win-x86_64OKJan 06 2025
R-4.3-mac-x86_64WARNINGJan 06 2025
R-4.3-mac-aarch64WARNINGJan 06 2025

Exports:cv.gglassocv.hsvmcv.logitcv.lscv.sqsvmgglasso

Dependencies:

Introduction to gglasso

Yi Yang

Rendered fromIntroduction_to_gglasso_package.Rmdusingknitr::rmarkdownon Jan 06 2025.

Last update: 2020-02-17
Started: 2018-04-19