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savvyGLM

Generalized Linear Models with Slab and Shrinkage Estimators

v0.1.4 · May 8, 2026 · GPL (>= 3)

Description

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>.

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Check History

OK 7 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE May 3, 2026

Dependency Network

Dependencies Reverse dependencies MASS glmnet expm glm2 Matrix CVXR savvyGLM

Version History

4 tracked
updated 0.1.4 ← 0.1.3 diff May 8, 2026
updated 0.1.3 ← 0.1.1 diff May 3, 2026
update 0.1.2 ← 0.1.1 diff May 2, 2026
new 0.1.1 May 2, 2026