Package: g.ridge 1.0
Takeshi Emura
g.ridge: Generalized Ridge Regression for Linear Models
Ridge regression due to Hoerl and Kennard (1970)<doi:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<doi:10.1080/03610918.2016.1193195> with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.
Authors:
g.ridge_1.0.tar.gz
g.ridge_1.0.tar.gz(r-4.5-noble)g.ridge_1.0.tar.gz(r-4.4-noble)
g.ridge_1.0.tgz(r-4.4-emscripten)g.ridge_1.0.tgz(r-4.3-emscripten)
g.ridge.pdf |g.ridge.html✨
g.ridge/json (API)
| # Install 'g.ridge' in R: |
| install.packages('g.ridge', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 11 months agofrom:f91de599e3. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 01 2024 |
| R-4.5-linux | OK | Nov 01 2024 |
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| g.ridge (generalized ridge regression) | g.ridge |
| GCV (generalized cross-validation) | GCV |
| X.mat (generating a design matrix) | X.mat |