Package: g.ridge 1.0

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:Takeshi Emura [aut, cre], Szu-Peng Yang [ctb]

g.ridge_1.0.tar.gz
g.ridge_1.0.zip(r-4.7)g.ridge_1.0.zip(r-4.6)g.ridge_1.0.zip(r-4.5)
g.ridge_1.0.tgz(r-4.6-any)g.ridge_1.0.tgz(r-4.5-any)
g.ridge_1.0.tar.gz(r-4.7-any)g.ridge_1.0.tar.gz(r-4.6-any)
g.ridge_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
g.ridge/json (API)

# Install 'g.ridge' in R:
install.packages('g.ridge', repos = c('https://takeshiemura1.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 206 downloads 3 exports 0 dependencies

Last updated from:f91de599e3. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK98
source / vignettesOK124
linux-release-x86_64OK92
macos-release-arm64OK86
macos-oldrel-arm64OK111
windows-develOK59
windows-releaseOK62
windows-oldrelOK53
wasm-releaseOK78

Exports:g.ridgeGCVX.mat

Dependencies: