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drf (version 1.3.1)

Distributional Random Forests

Description

An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (2022) .

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Install

install.packages('drf')

Monthly Downloads

459

Version

1.3.1

License

GPL-3

Issues

12

Pull Requests

2

Stars

45

Forks

10

Maintainer

Jeffrey Naf

Last Published

February 3rd, 2026

Functions in drf (1.3.1)

predict.drf

Predict from Distributional Random Forests object
medianHeuristic

Compute the median heuristic for the MMD bandwidth choice
plot.drf_tree

Plot a DRF tree object.
get_sample_weights

Given a trained forest and test data, compute the training sample weights for each test point.
drf

Distributional Random Forests
leaf_stats.drf

Calculate summary stats given a set of samples for regression forests.
get_tree

Retrieve a single tree from a trained forest object.
leaf_stats.default

A default leaf_stats for forests classes without a leaf_stats method that always returns NULL.
export_graphviz

Export a tree in DOT format. This function generates a GraphViz representation of the tree, which is then written into `dot_string`.
create_dot_body

Writes each node information If it is a leaf node: show it in different color, show number of samples, show leaf id If it is a non-leaf node: show its splitting variable and splitting value
print.drf_tree

Print a DRF tree object.
print.drf

Print a DRF forest object.
variableImportance

Variable importance based on MMD
variable_importance

Calculate a simple measure of 'importance' for each feature.
weighted.quantile

Weighted quantiles
split_frequencies

Calculate which features the forest split on at each depth.