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grf (version 2.0.1)

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.

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Install

install.packages('grf')

Monthly Downloads

7,924

Version

2.0.1

License

GPL-3

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Repository

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

Maintainer

Julie Tibshirani

Last Published

July 6th, 2021

Functions in grf (2.0.1)

best_linear_projection

Estimate the best linear projection of a conditional average treatment effect using a causal forest, or causal survival forest.

average_partial_effect

Average partial effect (removed)

causal_survival_forest

Causal survival forest (experimental)

Custom forest (removed)

boosted_regression_forest

Boosted regression forest (experimental)

average_treatment_effect

Get doubly robust estimates of average treatment effects.

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 LATE (removed)

Compute pseudo outcomes (based on the influence function of tau(X) for each unit) used for CART splitting.

expected_survival

Compute E[T | X]

export_graphviz

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

generate_causal_survival_data

Simulate causal survival data

generate_causal_data

Generate causal forest data

leaf_stats.regression_forest

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

get_scores.causal_forest

Compute doubly robust scores for a causal forest.

get_scores.causal_survival_forest

Compute doubly robust scores for a causal survival forest.

ll_regression_forest

Local linear forest

leaf_stats.causal_forest

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

get_scores.instrumental_forest

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

get_scores.multi_arm_causal_forest

Compute doubly robust scores for a multi arm causal forest.

Compute doubly robust scores for a GRF forest object

leaf_stats.instrumental_forest

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

get_sample_weights

Retrieve forest weights (renamed to get_forest_weights)

leaf_stats.default

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

predict.ll_regression_forest

Predict with a local linear forest

predict.multi_arm_causal_forest

Predict with a multi arm causal forest

predict.instrumental_forest

Predict with an instrumental forest

predict.causal_survival_forest

Predict with a causal survival forest forest

predict.quantile_forest

Predict with a quantile forest

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

predict.regression_forest

Predict with a regression forest

predict.boosted_regression_forest

Predict with a boosted regression forest.

predict.causal_forest

Predict with a causal forest

probability_forest

Probability forest

print.tuning_output

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

multi_arm_causal_forest

Multi-arm causal forest (experimental)

get_forest_weights

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

instrumental_forest

Intrumental forest

tune_causal_forest

Causal forest tuning (removed)

test_calibration

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

predict.multi_regression_forest

Predict with a multi regression forest

tune_instrumental_forest

Instrumental forest tuning (removed)

print.boosted_regression_forest

Print a boosted regression forest

tune_ll_regression_forest

Local linear forest tuning

predict.survival_forest

Predict with a survival forest

tune_ll_causal_forest

Local linear forest tuning

Print a GRF forest object.

survival_forest

Survival forest

Print a GRF tree object.

split_frequencies

Calculate which features the forest split on at each depth.

Retrieve a single tree from a trained forest object.

Find the leaf node for a test sample.

tune_regression_forest

Regression forest tuning (removed)

regression_forest

Regression forest

multi_regression_forest

Multi-task regression forest

grf: Generalized Random Forests

Plot a GRF tree object.

quantile_forest

Quantile forest

predict.probability_forest

Predict with a probability forest

variable_importance

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