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grf (version 2.2.1)

Generalized Random Forests

Description

Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.

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Install

install.packages('grf')

Monthly Downloads

7,924

Version

2.2.1

License

GPL-3

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Repository

https://github.com/grf-labs/grf

Maintainer

Julie Tibshirani

Last Published

December 14th, 2022

Functions in grf (2.2.1)

boosted_regression_forest

Boosted regression forest

Simple clustered bootstrap.

get_forest_weights

Given a trained forest and test data, compute the kernel weights for each test point.

generate_causal_survival_data

Simulate causal survival data

Compute rate estimates, a function to be passed on to bootstrap routine.

expected_survival

Compute E[T | X]

get_sample_weights

Retrieve forest weights (renamed to get_forest_weights)

Find the leaf node for a test sample.

generate_causal_data

Generate causal forest data

export_graphviz

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

Retrieve a single tree from a trained forest object.

get_scores.multi_arm_causal_forest

Compute doubly robust scores for a multi arm causal forest.

get_scores.causal_survival_forest

Compute doubly robust scores for a causal survival forest.

get_scores.instrumental_forest

Doubly robust scores for estimating the average conditional local average treatment effect.

leaf_stats.default

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

leaf_stats.causal_forest

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

grf: Generalized Random Forests

instrumental_forest

Intrumental forest

leaf_stats.instrumental_forest

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

get_scores.causal_forest

Compute doubly robust scores for a causal forest.

Compute doubly robust scores for a GRF forest object

leaf_stats.regression_forest

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

multi_regression_forest

Multi-task regression forest

predict.causal_forest

Predict with a causal forest

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

predict.causal_survival_forest

Predict with a causal survival forest forest

predict.boosted_regression_forest

Predict with a boosted regression forest.

plot.rank_average_treatment_effect

Plot the Targeting Operator Characteristic curve.

multi_arm_causal_forest

Multi-arm causal forest

Plot a GRF tree object.

predict.regression_forest

Predict with a regression forest

predict.quantile_forest

Predict with a quantile forest

ll_regression_forest

Local linear forest

predict.lm_forest

Predict with a lm forest

predict.multi_arm_causal_forest

Predict with a multi arm causal forest

predict.multi_regression_forest

Predict with a multi regression forest

predict.probability_forest

Predict with a probability forest

rank_average_treatment_effect.fit

Fitter function for Rank-Weighted Average Treatment Effect (RATE).

rank_average_treatment_effect

Estimate a Rank-Weighted Average Treatment Effect (RATE).

quantile_forest

Quantile forest

probability_forest

Probability forest

predict.ll_regression_forest

Predict with a local linear forest

predict.instrumental_forest

Predict with an instrumental forest

split_frequencies

Calculate which features the forest split on at each depth.

print.boosted_regression_forest

Print a boosted regression forest

regression_forest

Regression forest

variable_importance

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

predict.survival_forest

Predict with a survival forest

tune_instrumental_forest

Instrumental forest tuning (removed)

tune_ll_causal_forest

Local linear forest tuning

print.rank_average_treatment_effect

Print the Rank-Weighted Average Treatment Effect (RATE).

print.tuning_output

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

survival_forest

Survival forest

test_calibration

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

tune_regression_forest

Regression forest tuning (removed)

tune_ll_regression_forest

Local linear forest tuning

Print a GRF tree object.

Print a GRF forest object.

tune_causal_forest

Causal forest tuning (removed)

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.

average_partial_effect

Average partial effect (removed)

best_linear_projection

Estimate the best linear projection of a conditional average treatment effect.

average_treatment_effect

Get doubly robust estimates of average treatment effects.

Average LATE (removed)

Custom forest (removed)

causal_survival_forest

Causal survival forest