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 'ShrinkageRR' where the intensity
is chosen by minimizing an explicit Mean Squared Error (MSE) criterion.
Methods are based on Asimit, V., Cidota, M. A., Chen, Z., and Asimit, J. (2025)
<https://openaccess.city.ac.uk/id/eprint/35005/>.
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
Matrix, glmnet, MASS, expm, mnormt, stats |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-03-08 |
| DOI: |
10.32614/CRAN.package.savvySh |
| 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> |
| BugReports: |
https://github.com/ziwei-chenchen/savvySh/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://ziwei-chenchen.github.io/savvySh/ |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
savvySh results |
Documentation:
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