Package: gglasso 1.5.1

Yi Yang

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 [aut, cre], Hui Zou [aut], Sahir Bhatnagar [aut]

gglasso_1.5.1.tar.gz
gglasso_1.5.1.tar.gz(r-4.5-noble)gglasso_1.5.1.tar.gz(r-4.4-noble)
gglasso_1.5.1.tgz(r-4.4-emscripten)gglasso_1.5.1.tgz(r-4.3-emscripten)
gglasso.pdf |gglasso.html✨
gglasso/json (API)

# Install 'gglasso' in R:
install.packages('gglasso', repos = c('https://cran.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.

6.48 score 3 stars 9 packages 248 scripts 930 downloads 3 mentions 6 exports 0 dependencies

Last updated 8 months agofrom:f17ebf34e8. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-linux-x86_64OKNov 18 2024

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

Dependencies:

Introduction to gglasso

Yi Yang

Rendered fromIntroduction_to_gglasso_package.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2020-03-18
Started: 2020-03-18