Regularised and Information-Theoretic Sufficient Dimension Reduction // Implements covariance-stabilised sufficient dimension reduction for continuous responses with information- theoretic structural dimension selection. Supported methods include sliced inverse regression, sliced average variance estimation, directional regression, and principal Hessian directions. Sample, ridge, Oracle Approximating Shrinkage, Ledoit-Wolf, and Maximum Entropy Covariance (MEC) estimators are provided alongside prediction, resampling, simulation, and diagnostic utilities. The sufficient dimension reduction methods build on Li (1991) doi:10.1080/01621459.1991.10475035, Li (1992) doi:10.1080/01621459.1992.10476258, and Li and Wang (2007) doi:10.1198/016214507000000536. Covariance shrinkage follows Olorede and Yahya (2019) doi:10.48550/arXiv.1909.13017, Ledoit and Wolf (2004) doi:10.1016/S0047-259X(03)00096-4 and Chen et al. (2010) doi:10.1109/TSP.2010.2053029.