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.
| Version: | 0.1.1 |
| 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 |
| Published: | 2026-07-19 |
| DOI: | 10.32614/CRAN.package.ppforest2 (may not be active yet) |
| Author: | Andrés Vidal [aut, cre, cph], Natalia da Silva [aut] |
| Maintainer: | Andrés Vidal <andres at andresvidal.dev> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| Materials: | README, NEWS |
| CRAN checks: | ppforest2 results |
| Reference manual: | ppforest2.html , ppforest2.pdf |
| Vignettes: |
Custom strategies (source, R code) Introduction to ppforest2 (source, R code) |
| Package source: | ppforest2_0.1.1.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): ppforest2_0.1.1.tgz, r-oldrel (arm64): ppforest2_0.1.1.tgz, r-release (x86_64): ppforest2_0.1.1.tgz, r-oldrel (x86_64): ppforest2_0.1.1.tgz |
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