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ppforest2  

Projection Pursuit Oblique Decision Trees and Random Forests
View on CRAN: Click here


Download and install ppforest2 package within the R console
Install from CRAN:
install.packages("ppforest2")

Install from Github:
library("remotes")
install_github("cran/ppforest2")

Install by package version:
library("remotes")
install_version("ppforest2", "0.1.2")



Attach the package and use:
library("ppforest2")
Maintained by
Andrés Vidal
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-07-21
Latest Update: 2026-07-21
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.
How to cite:
Andrés Vidal (2026). ppforest2: Projection Pursuit Oblique Decision Trees and Random Forests. R package version 0.1.2, https://cran.r-project.org/web/packages/ppforest2. Accessed 22 Sep. 2026.
Previous versions and publish date:
(2026-07-21 10:20), 0.1.1 (2026-07-19 15:10)
Other packages that cited ppforest2 R package
View ppforest2 citation profile
Other R packages that ppforest2 depends, imports, suggests or enhances
Complete documentation for ppforest2
Functions, R codes and Examples using the ppforest2 R package
Full ppforest2 package functions and examples
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