Random Forest Super Greedy Trees R package randomForestSGT version 1.0.0 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) <doi:10.1007/s10462-026-11541-6>. Software Depends: R (>= 4.3.0) Imports: randomForestSRC (>= 3.6.2), varPro (>= 3.1.0) Suggests: mlbench, interp, glmnet Udaya B. Kogalur <ubk@kogalur.com> Comprehensive R Archive Network (CRAN) Min Lu [aut], Udaya B. Kogalur [aut, cre], Hemant Ishwaran [aut] GPL (>= 3) 2026-05-11 application/tgz https://CRAN.R-project.org/package=randomForestSGT doi:10.32614/CRAN.package.randomForestSGT