Learn R Programming
⚠️There's a newer version (2.3.2) of this package.Take me there.

grf (version 1.2.0)

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, survival regression and treatment effect estimation (optionally using instrumental variables), with support for missing values.

Copy Link

Version

Install

install.packages('grf')

Monthly Downloads

6,886

Version

1.2.0

License

GPL-3

Issues

55

Pull Requests

0

Stars

1,013

Forks

259

Maintainer

Julie Tibshirani

Last Published

June 4th, 2020

Functions in grf (1.2.0)

average_partial_effect

Estimate average partial effects using a causal forest
best_linear_projection

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

Causal forest
get_sample_weights

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

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

Plot a GRF tree object.
instrumental_forest

Intrumental forest
predict.custom_forest

Predict with a custom forest.
leaf_stats.causal_forest

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

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

Predict with a boosted regression forest.
ll_regression_forest

Local Linear forest
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.instrumental_forest

Predict with an instrumental forest
predict.causal_forest

Predict with a causal forest
print.boosted_regression_forest

Print a boosted regression forest
tune_causal_forest

Causal forest tuning
test_calibration

Omnibus evaluation of the quality of the random forest estimates via calibration.
predict.regression_forest

Predict with a regression forest
regression_forest

Regression forest
predict.survival_forest

Predict with a survival forest forest
quantile_forest

Quantile forest
print.grf

Print a GRF forest object.
split_frequencies

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

Survival forest
print.grf_tree

Print a GRF tree object.
tune_ll_causal_forest

Local linear forest tuning
tune_ll_regression_forest

Local linear forest tuning
print.tuning_output

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

Retrieve a single tree from a trained forest object.
variable_importance

Calculate a simple measure of 'importance' for each feature.
tune_regression_forest

Regression forest tuning
grf

GRF
tune_forest

Tune a forests
predict.ll_regression_forest

Predict with a local linear forest
tune_instrumental_forest

Instrumental forest tuning
predict.quantile_forest

Predict with a quantile 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`.
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
boosted_regression_forest

Boosted regression forest (experimental)
custom_forest

Custom forest
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 If trained with missing values, the edge arrow is filled according to which direction the NAs are sent.