Package: gcdnet 1.0.6
Yi Yang
gcdnet: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm
Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.
Authors:
gcdnet_1.0.6.tar.gz
gcdnet_1.0.6.zip(r-4.7-x86_64)gcdnet_1.0.6.zip(r-4.7-arm64)gcdnet_1.0.6.zip(r-4.6-x86_64)gcdnet_1.0.6.zip(r-4.6-arm64)
gcdnet_1.0.6.tar.gz(r-4.7-arm64)gcdnet_1.0.6.tar.gz(r-4.7-x86_64)gcdnet_1.0.6.tar.gz(r-4.6-arm64)gcdnet_1.0.6.tar.gz(r-4.6-x86_64)
gcdnet_1.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gcdnet/json (API)
| # Install 'gcdnet' in R: |
| install.packages('gcdnet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/emeryyi/gcdnet/issues
- FHT - FHT data introduced in Friedman et al. (2010).
Last updated from:4e14d2e326. Checks:9 OK, 1 WARNING. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 90 | ||
| linux-devel-x86_64 | OK | 118 | ||
| source / vignettes | OK | 176 | ||
| linux-release-arm64 | OK | 86 | ||
| linux-release-x86_64 | OK | 102 | ||
| windows-devel-arm64 | OK | 216 | ||
| windows-devel-x86_64 | OK | 101 | ||
| windows-release-arm64 | WARNING | 206 | ||
| windows-release-x86_64 | OK | 77 | ||
| wasm-release | OK | 86 |
Exports:coefcv.erpathcv.gcdnetcv.hsvmpathcv.logitpathcv.lspathcv.sqsvmpathgcdnetpredict
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract Model Coefficients | coef |
| Get coefficients or make coefficient predictions from a "cv.gcdnet" object. | coef.cv.gcdnet |
| Get coefficients or make coefficient predictions from a "gcdnet" object. | coef.erpath coef.gcdnet coef.hsvmpath coef.logitpath coef.lspath coef.sqsvmpath |
| Cross-validation for gcdnet | cv.erpath cv.gcdnet cv.hsvmpath cv.logitpath cv.lspath cv.sqsvmpath |
| FHT data introduced in Friedman et al. (2010). | FHT |
| Fits the regularization paths for large margin classifiers | gcdnet |
| Plot the cross-validation curve produced by cv.gcdnet | plot.cv.gcdnet |
| Plot coefficients from a "gcdnet" object | plot.gcdnet |
| Model predictions | predict |
| Make predictions from a "cv.gcdnet" object. | predict.cv.gcdnet |
| Make predictions from a "gcdnet" object | predict.erpath predict.gcdnet predict.hsvmpath predict.logitpath predict.lspath predict.sqsvmpath |
| Print a gcdnet object | print.gcdnet |