Computes feature-specific R-squared (R2) contributions for
boosting tree models using a Shapley-value-based decomposition of the
total R-squared in polynomial time. Supports models fitted with
'XGBoost', 'LightGBM', and 'CatBoost', with optimized backend-specific
implementations and cached tree summaries suitable for large-scale
problems. Multiple
visualization tools are included for interpreting and communicating
feature contributions. The methodology is described in Jiang, Zhang,
and Zhang (2025)