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ppforest2

ppforest2 provides projection pursuit oblique decision trees and random forests for classification. Instead of splitting on single variables, each node projects the data onto a linear combination of features, capturing structure that axis-aligned trees miss.

The package wraps a high-performance C++ core and is intended as a modern successor to PPforest.

Key capabilities: oblique splits via projection pursuit, multi-threaded forest training (OpenMP), cross-platform reproducibility, three variable importance measures (projection-based, weighted, permutation), LDA/PDA optimisation, OOB error estimation, and parsnip / tidymodels integration.

Installation

# install.packages("devtools")
devtools::install_github("andres-vidal/ppforest2", subdir = "bindings/R", build = FALSE)

Usage

Single tree

library(ppforest2)

model <- pptr(Species ~ ., data = iris)
predict(model, iris[1:5, ])
summary(model)

Random forest

forest <- pprf(Species ~ ., data = iris, size = 500)
predict(forest, iris[1:5, ])
predict(forest, iris[1:5, ], type = "prob")   # vote proportions
summary(forest)

Regularisation (PDA)

When classes are highly correlated or the number of variables is large relative to the sample size, penalised discriminant analysis can improve separation:

pptr(Species ~ ., data = iris, lambda = 0.5)

Visualisation

ppforest2 provides four diagnostic plot types (requires ggplot2):

# Mosaic overview: structure + importance + boundaries
plot(model)

# Individual plot types
plot(model, type = "structure")     # tree diagram with per-node histograms
plot(model, type = "importance")    # variable importance bar chart
plot(model, type = "projection")    # projected data at each split
plot(model, type = "boundaries")    # decision boundaries in feature space

# Forest: importance across all trees, or inspect individual trees
plot(forest)
plot(forest, type = "structure", tree_index = 1)
plot(forest, type = "boundaries", tree_index = 1)

tidymodels integration

ppforest2 integrates with parsnip:

library(parsnip)

# Single tree
spec <- pp_tree(lambda = 0) |> set_engine("ppforest2") |> set_mode("classification")
fit  <- fit(spec, Species ~ ., data = iris)

# Random forest
spec <- pp_rand_forest(trees = 50, mtry = 2) |> set_engine("ppforest2")
fit  <- spec |> fit(Species ~ ., data = iris)
predict(fit, iris, type = "prob")

JSON serialisation

Models can be saved and loaded in JSON format, enabling interoperability with the C++ CLI and other language bindings:

save_json(model, "model.json")
restored <- load_json("model.json")

Learning more

• vignette("introduction") — a tutorial covering trees, forests, visualisation, and tidymodels integration.
• C++ API Reference — core library documentation (Doxygen).
• GitHub repository — source code, build instructions, and benchmarks.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.