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:
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')) |
Bug tracker:https://github.com/emeryyi/gglasso/issues
Datasets:
Last updated 7 months agofrom:f17ebf34e8. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 19 2024 |
| R-4.5-linux-x86_64 | OK | Sep 19 2024 |
Exports:cv.gglassocv.hsvmcv.logitcv.lscv.sqsvmgglasso
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simplified gene expression data from Scheetz et al. (2006) | bardet |
| get coefficients or make coefficient predictions from a "cv.gglasso" object. | coef.cv.gglasso |
| get coefficients or make coefficient predictions from an "gglasso" object. | coef.gglasso |
| Simplified gene expression data from Alon et al. (1999) | colon |
| Cross-validation for gglasso | cv.gglasso cv.hsvm cv.logit cv.ls cv.sqsvm |
| Fits the regularization paths for group-lasso penalized learning problems | gglasso |
| plot the cross-validation curve produced by cv.gglasso | plot.cv.gglasso |
| Plot solution paths from a "gglasso" object | plot.gglasso |
| make predictions from a "cv.gglasso" object. | predict.cv.gglasso |
| make predictions from a "gglasso" object. | predict.gglasso |
| print a gglasso object | print.gglasso |
