# Projection Pursuit Oblique Decision Trees and Random Forests

## Documentation for package ‘ppforest2’ version 0.1.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

bag_samples | In-bag row indices per tree.
binarize_disabled | Disabled binarization strategy (placeholder).
binarize_largest_gap | Largest-gap binarization strategy.
california_housing | California Housing Dataset
crab | Australian Crabs Dataset
crabs | Crabs Dataset
cutpoint_mean_of_means | Mean-of-means split cutpoint strategy.
fishcatch | Fish Catch Dataset
fitted.ppmodel | Fitted (in-sample) predictions from a ppforest2 model.
formula.ppmodel | Formula extractor for ppforest2 models.
glass | Glass Dataset
grouping_by_cutpoint | Cutpoint-based grouping strategy (regression).
grouping_by_label | Label-based grouping strategy.
image | Image Dataset
leaf_majority_vote | Majority-vote leaf strategy.
leaf_mean_response | Mean-response leaf strategy.
leukemia | Leukemia Dataset
load_json | Load a model from a JSON file.
lymphoma | Lymphoma Dataset
NCI60 | NCI60 Dataset
nobs.ppmodel | Number of observations used to fit a ppforest2 model.
olive | Olive Dataset
oob_error | Out-of-bag error for a random forest.
oob_predictions | Out-of-bag predictions for a random forest.
oob_samples | Out-of-bag row indices per tree.
parkinson | Parkinson Dataset
permuted_importance | Permuted variable importance for a random forest.
plot.pprf | Plot a pprf model.
plot.pptr | Plot a pptr model.
pprf | Trains a Random Forest of Projection-Pursuit oblique decision trees.
pptr | Trains a Projection-Pursuit oblique decision tree.
pp_pda | PDA projection pursuit strategy.
pp_rand_forest | Parsnip model specification for pprf.
pp_tree | Parsnip model specification for pptr.
predict.pprf_classification | Predicts labels or vote proportions from a pprf model (classification mode).
predict.pprf_regression | Predicts numeric responses from a pprf model (regression mode).
predict.pptr_classification | Predicts labels or per-group one-hot proportions from a pptr model (classification mode).
predict.pptr_regression | Predicts numeric responses from a pptr model (regression mode).
print.pprf | Prints a compact summary of a pprf forest.
print.pptr | Prints the structure of a pptr tree.
projection_importance | Projection-coefficient variable importance.
residuals.ppmodel | Residuals from a regression ppforest2 model.
save_json | Save a model to a JSON file.
save_json.ppmodel | Save a model to a JSON file.
stop_any | Composite stopping rule (logical OR).
stop_max_depth | Maximum-depth stopping rule.
stop_min_size | Minimum-size stopping rule.
stop_min_variance | Minimum-variance stopping rule.
stop_pure_node | Pure-node stopping rule.
summary.pprf | Summary of a pprf forest (shared header + VI).
update.pp_rand_forest | Update a 'pp_rand_forest' model specification.
update.pp_tree | Update a 'pp_tree' model specification.
vars_all | All-variables selection strategy.
vars_uniform | Uniform random variable selection strategy.
weighted_importance | Weighted projection variable importance for a random forest.
wine | Wine Dataset