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The Graphical Group Ridge 'GGRidge' package package classifies ridge regression predictors in disjoint groups of conditionally correlated variables and derives different penalties (shrinkage parameters) for these groups of predictors. It combines the ridge regression method with the graphical model for high-dimensional data (i.e. the number of predictors exceeds the number of cases) or ill-conditioned data (e.g. in the presence of multicollinearity among predictors). The package reduces the mean square errors and the extent of over-shrinking of predictors as compared to the ridge method.Aldahmani, S. and Zoubeidi, T. (2020) <doi:10.1080/00949655.2020.1803320>.
| Version: | 1.1.0 |
| Imports: | gRbase, CVglasso, MASS |
| Published: | 2023-10-06 |
| DOI: | 10.32614/CRAN.package.GGRidge |
| Author: | Saeed Aldahmani [aut, cre, cph], Taoufik Zoubeidi [ths] |
| Maintainer: | Saeed Aldahmani <saldahmani at uaeu.ac.ae> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | GGRidge results |
| Reference manual: | GGRidge.pdf |
| Package source: | GGRidge_1.1.0.tar.gz |
| Windows binaries: | r-devel: GGRidge_1.1.0.zip, r-release: GGRidge_1.1.0.zip, r-oldrel: GGRidge_1.1.0.zip |
| macOS binaries: | r-release (arm64): GGRidge_1.1.0.tgz, r-oldrel (arm64): GGRidge_1.1.0.tgz, r-release (x86_64): GGRidge_1.1.0.tgz, r-oldrel (x86_64): GGRidge_1.1.0.tgz |
| Old sources: | GGRidge archive |
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