Projection Pursuit Oblique Decision Trees and Random Forests R package ppforest2 version 0.1.2 Builds decision trees by splitting on linear combinations of randomly chosen variables. Projection pursuit is used to choose a projection of the variables that best separates the groups. Using linear combinations of variables to separate groups takes the correlation between variables into account, which allows the model to outperform a traditional decision tree when the separation between groups occurs in combinations of variables. Single trees can be assembled into random forests for improved accuracy. Implements projection pursuit classification trees (Lee, Cook, Park and Lee (2013) <doi:10.1214/13-EJS810>) and projection pursuit forests (da Silva, Cook and Lee (2021) <doi:10.1080/10618600.2020.1870480>), following the earlier 'PPforest' package. Software Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.11) LinkingTo: Rcpp, RcppEigen Suggests: ggplot2, jsonlite, knitr, parsnip, patchwork, rlang, rmarkdown, rsample, testthat (>= 3.0.0), tibble, tune, vdiffr, withr, workflows, yardstick Andrés Vidal <andres@andresvidal.dev> Comprehensive R Archive Network (CRAN) Andrés Vidal [aut, cre, cph], Natalia da Silva [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=ppforest2/LICENSE) 2026-07-21 application/tgz https://CRAN.R-project.org/package=ppforest2 doi:10.32614/CRAN.package.ppforest2