ziwei-chenchen.r-universe.dev

[ziwei-chenchen] savvyGLM 0.1.4

ziwei.chen.3@citystgeorges.ac.uk (Ziwei Chen) — Sun, 10 May 2026 12:27:31 GMT

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) , Asimit et al. (2025) , Ledoit and Wolf (2004) , and Ledoit and Wolf (2022) .

[ziwei-chenchen] savvyPR 0.1.0

ziwei.chen.3@citystgeorges.ac.uk (Ziwei Chen) — Wed, 18 Mar 2026 17:11:33 GMT

Implements the Savvy Parity Regression 'savvyPR' methodology for multivariate linear regression analysis. The package solves an optimization problem that balances the contribution of each predictor variable to ensure estimation stability in the presence of multicollinearity. It supports two distinct parameterization methods, a Budget-based approach that allocates a fixed loss contribution to each predictor, and a Target-based approach (t-tuning) that utilizes a relative elasticity weight for the response variable. The package provides comprehensive tools for model estimation, risk distribution analysis, and parameter tuning via cross-validation (PR1, PR2, and PR3 model types) to optimize predictive accuracy. Methods are based on Asimit, Chen, Ichim and Millossovich (2026) .

[ziwei-chenchen] savvySh 0.1.1

ziwei.chen.3@citystgeorges.ac.uk (Ziwei Chen) — Wed, 18 Mar 2026 16:50:39 GMT

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) .