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grf (version 0.10.3)

Generalized Random Forests (Beta)

Description

A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables). This package is currently in beta, and we expect to make continual improvements to its performance and usability.

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Version

Install

install.packages('grf')

Monthly Downloads

6,410

Version

0.10.3

License

GPL-3

Issues

60

Pull Requests

1

Stars

1,037

Forks

262

Maintainer

Julie Tibshirani

Last Published

May 27th, 2019

Functions in grf (0.10.3)

get_tree

Retrieve a single tree from a trained forest object.
grf

GRF
export_graphviz

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

Estimate average treatment effects using a causal forest
variable_importance

Calculate a simple measure of 'importance' for each feature.
merge_forests

Merges a list of forests that were grown using the same data into one large forest.
get_sample_weights

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

Plot a GRF tree object.
test_calibration

Omnibus evaluation of the quality of the random forest estimates via calibration.
predict.custom_forest

Predict with a custom forest.
predict.instrumental_forest

Predict with an instrumental forest
predict.boosted_regression_forest

Predict with a boosted regression forest.
ll_regression_forest

Local Linear forest
custom_forest

Custom forest
predict.ll_regression_forest

Predict with a local linear forest
tune_causal_forest

Causal forest tuning
causal_forest

Causal forest
boosted_regression_forest

Boosted regression forest (experimental)
print.grf

Print a GRF forest object.
instrumental_forest

Intrumental forest
print.tuning_output

Print tuning output. Displays average error for q-quantiles of tuned parameters.
print.grf_tree

Print a GRF tree object.
quantile_forest

Quantile forest
predict.quantile_forest

Predict with a quantile forest
predict.regression_forest

Predict with a regression forest
tune_regression_forest

Regression forest tuning
print.boosted_regression_forest

Print a boosted regression forest
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
predict.causal_forest

Predict with a causal forest
regression_forest

Regression forest
split_frequencies

Calculate which features the forest split on at each depth.
tune_ll_regression_forest

Local linear forest tuning
average_partial_effect

Estimate average partial effects using a causal forest