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grf (version 0.9.5)

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

0.9.5

License

GPL-3

Issues

55

Pull Requests

0

Stars

1,013

Forks

259

Maintainer

Julie Tibshirani

Last Published

January 9th, 2018

Functions in grf (0.9.5)

predict.custom_forest

Predict with a custom forest.
estimate_average_effect

Estimate average treatment effects using a causal forest
get_tree

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

Predict with a quantile forest
predict.regression_forest

Predict with a regression forest
quantile_forest

Quantile forest
regression_forest

Regression forest
causal_forest

Causal forest
custom_forest

Custom forest
instrumental_forest

Intrumental forest
predict.causal_forest

Predict with a causal forest
split_frequencies

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

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

Predict with an instrumental forest
print.grf

Print a GRF forest object.
print.grf_tree

Print a GRF tree object.