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: 2026-07-21 12:27:31 UTC; root
Author: Andrés Vidal [aut, cre, cph], Natalia da Silva [aut]
Maintainer: Andrés Vidal <andres@andresvidal.dev>
Repository: https://andres-vidal.r-universe.dev
Date/Publication: 2026-07-21 08:20:02 UTC
RemoteUrl: https://github.com/cran/ppforest2
RemoteRef: HEAD
RemoteSha: 625e145edb4f964acbe195475c35da26462436cd