Slab and Shrinkage Linear Regression Estimation R package savvySh version 0.1.1 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/>. Software Depends: R (>= 3.6.0) Imports: Matrix, glmnet, MASS, expm, mnormt, stats Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Ziwei Chen <Ziwei.Chen.3@citystgeorges.ac.uk> Comprehensive R Archive Network (CRAN) Ziwei Chen [aut, cre] (ORCID: <https://orcid.org/0009-0009-6376-3850>), Vali Asimit [aut] (ORCID: <https://orcid.org/0000-0002-7706-0066>), Marina Anca Cidota [aut] (ORCID: <https://orcid.org/0009-0004-9505-7233>), Jennifer Asimit [aut] (ORCID: <https://orcid.org/0000-0002-4857-2249>) GPL (>= 3) 2026-03-08 application/tgz https://CRAN.R-project.org/package=savvySh doi:10.32614/CRAN.package.savvySh