Learn R Programming

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

grf (version 2.0.2)

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

7,924

Version

2.0.2

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

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

Maintainer

Julie Tibshirani

Last Published

July 14th, 2021

Functions in grf (2.0.2)

boosted_regression_forest

Boosted regression forest (experimental)

get_sample_weights

Retrieve forest weights (renamed to get_forest_weights)

get_scores.causal_survival_forest

Compute doubly robust scores for a causal survival forest.

leaf_stats.default

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

get_scores.causal_forest

Compute doubly robust scores for a causal forest.

predict.multi_regression_forest

Predict with a multi regression forest

predict.probability_forest

Predict with a probability forest

leaf_stats.instrumental_forest

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

Compute doubly robust scores for a GRF forest object

expected_survival

Compute E[T | X]

causal_survival_forest

Causal survival forest (experimental)

export_graphviz

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

ll_regression_forest

Local linear forest

leaf_stats.regression_forest

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

Average LATE (removed)

instrumental_forest

Intrumental forest

average_partial_effect

Average partial effect (removed)

multi_arm_causal_forest

Multi-arm causal forest (experimental)

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

generate_causal_data

Generate causal forest data

test_calibration

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

generate_causal_survival_data

Simulate causal survival data

tune_regression_forest

Regression forest tuning (removed)

tune_causal_forest

Causal forest tuning (removed)

variable_importance

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

predict.ll_regression_forest

Predict with a local linear forest

leaf_stats.causal_forest

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

predict.causal_survival_forest

Predict with a causal survival forest forest

average_treatment_effect

Get doubly robust estimates of average treatment effects.

best_linear_projection

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

Print a GRF forest object.

predict.instrumental_forest

Predict with an instrumental forest

predict.multi_arm_causal_forest

Predict with a multi arm causal forest

Retrieve a single tree from a trained forest object.

Print a GRF tree object.

quantile_forest

Quantile forest

regression_forest

Regression forest

survival_forest

Survival forest

probability_forest

Probability forest

print.tuning_output

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

split_frequencies

Calculate which features the forest split on at each depth.

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

grf: Generalized Random Forests

get_forest_weights

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

multi_regression_forest

Multi-task regression forest

Plot a GRF tree object.

Find the leaf node for a test sample.

print.boosted_regression_forest

Print a boosted regression forest

predict.survival_forest

Predict with a survival forest

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.

predict.boosted_regression_forest

Predict with a boosted regression forest.

predict.regression_forest

Predict with a regression forest

predict.quantile_forest

Predict with a quantile forest

predict.causal_forest

Predict with a causal forest

tune_instrumental_forest

Instrumental forest tuning (removed)

tune_ll_causal_forest

Local linear forest tuning

tune_ll_regression_forest

Local linear forest tuning

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.

Custom forest (removed)