Implements random forest Super Greedy Trees (SGTs) for
regression. SGTs extend classification and regression tree splitting
by fitting lasso-penalized local parametric models at tree nodes,
producing sparse univariate and multivariate geometric cuts such as
axis-aligned splits, hyperplanes, ellipsoids, hyperboloids, and
interaction-based cuts. Trees are grown best-split-first by
selecting cuts that reduce empirical risk, and ensembles provide
out-of-bag error estimation, prediction on new data, variable
filtering, tuning of the hcut complexity parameter,
coordinate-descent lasso fitting, variable importance, and local
coefficient summaries. For the underlying method,
see Ishwaran (2026)