v1.2.5 - The Anisotropic Tree Update

Added

• Topological Split Strategies (TreeEffect): Formalized the split_strategy parameter. Users can now explicitly toggle between 'uniform' (creating mathematically orthogonal, shift-invariant Cartesian grids) and 'quantile' (applying the empirical Probability Integral Transform to create density-adaptive partitions that perfectly balance sample distributions across all leaves).

Changed

• Empirical Sparsity-Adaptive Penalty (Anisotropic Ridge): Upgraded the structural penalty of the Random Forest (t(...)) and Linear Tree (lt(...)) modules. By setting sp_alpha > 0, the initialization pass now accurately records the empirical data density of each terminal leaf ($C_i$).
• Drift & Singularity Prevention: Starved or empty edge-boundary leaves now receive geometrically massive penalties. This guarantees global matrix rank, eliminates the catastrophic test drift associated with hard Cartesian grids, and theoretically resolves the OLS singularities traditionally found in Model-Based Recursive Partitioning (MOB).