An implementation of a regularized regression prediction and 
    empirical Bayes method to recover the true gene expression profile in 
    noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018) 
    <doi:10.1038/s41592-018-0033-z> for more details.

Provides a toolbox that allows the user to implement accessibility related concepts.

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>.

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)
    <https://openaccess.city.ac.uk/id/eprint/37017/>.