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savvySh: Slab and Shrinkage Linear Regression Estimation
Implements a suite of shrinkage estimators for multivariate linear
regression to improve estimation stability and predictive accuracy.
Provides methods including the Stein estimator, Diagonal Shrinkage,
the general Shrinkage estimator (solving a Sylvester equation), and
Slab Regression (Simple and Generalized). These methods address Stein's
paradox by introducing structured bias to reduce variance without requiring
cross-validation, except for Shrinkage Ridge Regression where the intensity
is chosen by minimizing an explicit Mean Squared Error (MSE) criterion.
Methods are based on paper
<https://openaccess.city.ac.uk/id/eprint/35005/>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
Matrix, glmnet, MASS, expm, mnormt, stats |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-03-03 |
| DOI: |
10.32614/CRAN.package.savvySh (may not be active yet) |
| Author: |
Ziwei Chen  [aut,
cre],
Vali Asimit [aut],
Marina Anca Cidota
[aut],
Jennifer Asimit
[aut] |
| Maintainer: |
Ziwei Chen <Ziwei.Chen.3 at citystgeorges.ac.uk> |
| License: |
GPL (≥ 3) |
| URL: |
https://ziwei-chenchen.github.io/savvySh/ |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
savvySh results |
Documentation:
Downloads:
Linking:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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