CRAN/E | shrink

shrink

Global, Parameterwise and Joint Shrinkage Factor Estimation

Installation

About

The predictive value of a statistical model can often be improved by applying shrinkage methods. This can be achieved, e.g., by regularized regression or empirical Bayes approaches. Various types of shrinkage factors can also be estimated after a maximum likelihood. While global shrinkage modifies all regression coefficients by the same factor, parameterwise shrinkage factors differ between regression coefficients. With variables which are either highly correlated or associated with regard to contents, such as several columns of a design matrix describing a nonlinear effect, parameterwise shrinkage factors are not interpretable and a compromise between global and parameterwise shrinkage, termed 'joint shrinkage', is a useful extension. A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA residuals can be applied. Global, parameterwise and joint shrinkage for models fitted by lm(), glm(), coxph(), or mfp() is available.

Citation shrink citation info
github.com/biometrician/shrink
Bug report File report

Key Metrics

Version 1.2.3
R ≥ 3.2.2
Published 2023-10-31 150 days ago
Needs compilation? no
License GPL-3
CRAN checks shrink results

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Maintainer

Maintainer

Daniela Dunkler

daniela.dunkler@meduniwien.ac.at

Authors

Daniela Dunkler

aut / cre

Georg Heinze

aut

Material

NEWS
Reference manual
Package source

Vignettes

JSS Example Code

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

shrink archive

Depends

R ≥ 3.2.2

Imports

survival
MASS
rms
mfp

Suggests

aod
knitr
rmarkdown