grf2.3.2 package

Generalized Random Forests

Average LATE (removed)

Average partial effect (removed)

Get doubly robust estimates of average treatment effects.

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

Boosted regression forest

Simple clustered bootstrap.

Causal forest

Causal survival forest

Writes each node information If it is a leaf node: show it in differen...

Custom forest (removed)

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

Compute E[T | X]

Export a tree in DOT format. This function generates a GraphViz repres...

Generate causal forest data

Simulate causal survival data

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

Find the leaf node for a test sample.

Retrieve forest weights (renamed to get_forest_weights)

Compute doubly robust scores for a causal forest.

Compute doubly robust scores for a causal survival forest.

Doubly robust scores for estimating the average conditional local aver...

Compute doubly robust scores for a multi arm causal forest.

Compute doubly robust scores for a GRF forest object

Retrieve a single tree from a trained forest object.

grf: Generalized Random Forests

Intrumental forest

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

A default leaf_stats for forests classes without a leaf_stats method t...

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

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

Local linear forest

LM Forest

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

Multi-arm/multi-outcome causal forest

Multi-task regression forest

Plot a GRF tree object.

Plot the Targeting Operator Characteristic curve.

Predict with a boosted regression forest.

Predict with a causal forest

Predict with a causal survival forest forest

Predict with an instrumental forest

Predict with a local linear forest

Predict with a lm forest

Predict with a multi arm causal forest

Predict with a multi regression forest

Predict with a probability forest

Predict with a quantile forest

Predict with a regression forest

Predict with a survival forest

Print a boosted regression forest

Print a GRF forest object.

Print a GRF tree object.

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

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

Probability forest

Quantile forest

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

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

Regression forest

Calculate which features the forest split on at each depth.

Survival forest

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

Causal forest tuning (removed)

Tune a forest

Instrumental forest tuning (removed)

Local linear forest tuning

Local linear forest tuning

Regression forest tuning (removed)

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

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.

Maintainer: Erik Sverdrup License: GPL-3 Last published: 2024-02-25