ppforest2: Projection Pursuit Oblique Decision Trees and Random Forests
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: |
0.1.2 |
| 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: |
2026-07-21 |
| DOI: |
10.32614/CRAN.package.ppforest2 |
| 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 |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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