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grf (version 0.9.4)

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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Install

install.packages('grf')

Monthly Downloads

6,886

Version

0.9.4

License

GPL-3

Issues

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Maintainer

Julie Tibshirani

Last Published

November 27th, 2017

Functions in grf (0.9.4)

predict.regression_forest

Predict with a regression forest
predict.quantile_forest

Predict with a quantile forest
instrumental_forest

Intrumental forest
predict.causal_forest

Predict with a causal forest
estimate_average_effect

Estimate average treatment effects using a causal forest
predict.custom_forest

Predict with a custom forest.
predict.instrumental_forest

Predict with an instrumental forest
get_tree

Retrieve a single tree from a trained forest object.
quantile_forest

Quantile forest
causal_forest

Causal forest
regression_forest

Regression forest
custom_forest

Custom forest
split_frequencies

Get summaries of which features the forest split on
print.grf

Print a GRF forest object.
print.grf_tree

Print a GRF tree object.