Package: ppforest2
Type: Package
Title: Projection Pursuit Oblique Decision Trees and Random Forests
Version: 0.1.2
Date: 2026-07-20
Authors@R: c(
    person("Andrés", "Vidal", email = "andres@andresvidal.dev", role = c("aut", "cre", "cph")),
    person("Natalia", "da Silva", role = "aut"))
Description: 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.
License: MIT + file LICENSE
Imports: Rcpp (>= 1.0.11)
LinkingTo: Rcpp, RcppEigen
Depends: R (>= 3.5.0)
LazyData: true
RoxygenNote: 7.3.2
Encoding: UTF-8
Suggests: ggplot2, jsonlite, knitr, parsnip, patchwork, rlang,
        rmarkdown, rsample, testthat (>= 3.0.0), tibble, tune, vdiffr,
        withr, workflows, yardstick
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/testthat/parallel: false
NeedsCompilation: yes
Packaged: 2026-07-20 19:36:31 UTC; andres
Author: Andrés Vidal [aut, cre, cph],
  Natalia da Silva [aut]
Maintainer: Andrés Vidal <andres@andresvidal.dev>
Repository: CRAN
Date/Publication: 2026-07-21 08:20:02 UTC
Built: R 4.6.1; x86_64-w64-mingw32; 2026-09-02 02:40:15 UTC; windows
Archs: x64
