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grf (version 0.9.6)

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

6,886

Version

0.9.6

License

GPL-3

Issues

54

Pull Requests

0

Stars

1,014

Forks

259

Maintainer

Julie Tibshirani

Last Published

April 14th, 2018

Functions in grf (0.9.6)

print.grf

Print a GRF forest object.
split_frequencies

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

Regression forest tuning
causal_forest

Causal forest
instrumental_forest

Intrumental forest
get_sample_weights

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

GRF
average_partial_effect

Estimate average partial effects using a causal forest
predict.causal_forest

Predict with a causal forest
average_treatment_effect

Estimate average treatment effects using a causal forest
predict.custom_forest

Predict with a custom forest.
custom_forest

Custom forest
get_tree

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

Predict with an instrumental forest
tune_causal_forest

Causal forest tuning
predict.regression_forest

Predict with a regression forest
regression_forest

Regression forest
variable_importance

Calculate a simple measure of 'importance' for each feature.
print.grf_tree

Print a GRF tree object.
predict.quantile_forest

Predict with a quantile forest
quantile_forest

Quantile forest