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grf (version 1.0.0)

Generalized Random Forests

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).

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Install

install.packages('grf')

Monthly Downloads

7,924

Version

1.0.0

License

GPL-3

Issues

54

Pull Requests

0

Stars

1,016

Forks

259

Maintainer

Julie Tibshirani

Last Published

December 1st, 2019

Functions in grf (1.0.0)

get_tree

Retrieve a single tree from a trained forest object.
leaf_stats.default

A default leaf_stats for forests classes without a leaf_stats method that always returns NULL.
leaf_stats.instrumental_forest

Calculate summary stats given a set of samples for instrumental forests.
predict.custom_forest

Predict with a custom forest.
predict.instrumental_forest

Predict with an instrumental forest
predict.ll_regression_forest

Predict with a local linear forest
predict.quantile_forest

Predict with a quantile forest
plot.grf_tree

Plot a GRF tree object.
causal_forest

Causal forest
merge_forests

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

Calculate summary stats given a set of samples for regression forests.
regression_forest

Regression forest
ll_regression_forest

Local Linear forest
grf

GRF
tune_ll_regression_forest

Local linear forest tuning
tune_ll_causal_forest

Local linear forest tuning
instrumental_forest

Intrumental forest
print.grf

Print a GRF forest object.
split_frequencies

Calculate which features the forest split on at each depth.
print.grf_tree

Print a GRF tree object.
leaf_stats.causal_forest

Calculate summary stats given a set of samples for causal forests.
tune_forest

Tune a forests
tune_instrumental_forest

Instrumental forest tuning
predict.boosted_regression_forest

Predict with a boosted regression forest.
variable_importance

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

Predict with a regression forest
tune_regression_forest

Regression forest tuning
print.boosted_regression_forest

Print a boosted regression forest
predict.causal_forest

Predict with a causal forest
test_calibration

Omnibus evaluation of the quality of the random forest estimates via calibration.
print.tuning_output

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

Causal forest tuning
quantile_forest

Quantile forest
boosted_regression_forest

Boosted regression forest (experimental)
best_linear_projection

Estimate the best linear projection of a conditional average treatment effect using a causal forest.
export_graphviz

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

Custom forest
average_late

Estimate the average (conditional) local average treatment effect using a causal forest.
average_treatment_effect

Estimate average treatment effects using a causal forest
get_sample_weights

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

Estimate average partial effects using a causal forest