Generalized Linear Models with Slab and Shrinkage Estimators // Provides a flexible framework for fitting generalized linear models (GLMs) with slab and shrinkage estimators. Methods include the Stein estimator (St), Diagonal Shrinkage (DSh), Simple Slab Regression (SR), Generalized Slab Regression (GSR), Ledoit-Wolf Linear Shrinkage (LW), Quadratic-Inverse Shrinkage (QIS), and Shrinkage (Sh), all integrated into the iteratively reweighted least squares (IRLS) algorithm. This approach enhances estimation accuracy, convergence, and robustness in the presence of multicollinearity. The best-fitting model is selected based on the Akaike Information Criterion (AIC). Methods are related to methods described in Marschner (2011) doi:10.32614/RJ-2011-012, Asimit et al. (2025) https://openaccess.city.ac.uk/id/eprint/35005/, Ledoit and Wolf (2004) doi:10.1016/S0047-259X(03)00096-4, and Ledoit and Wolf (2022) doi:10.3150/20-BEJ1315.