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grf (version 0.10.0)

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

6,962

Version

0.10.0

License

GPL-3

Issues

59

Pull Requests

1

Stars

1,031

Forks

261

Maintainer

Julie Tibshirani

Last Published

May 9th, 2018

Functions in grf (0.10.0)

split_frequencies

Calculate which features the forest split on at each depth.
predict.regression_forest

Predict with a regression forest
grf

GRF
custom_forest

Custom forest
average_partial_effect

Estimate average partial effects using a causal forest
get_tree

Retrieve a single tree from a trained forest object.
predict.causal_forest

Predict with a causal forest
predict.custom_forest

Predict with a custom forest.
predict.instrumental_forest

Predict with an instrumental forest
print.grf_tree

Print a GRF tree object.
regression_forest

Regression forest
quantile_forest

Quantile forest
causal_forest

Causal forest
get_sample_weights

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

Estimate average treatment effects using a causal forest
tune_causal_forest

Causal forest tuning
print.grf

Print a GRF forest object.
instrumental_forest

Intrumental forest
variable_importance

Calculate a simple measure of 'importance' for each feature.
predict.quantile_forest

Predict with a quantile forest
tune_regression_forest

Regression forest tuning