# Generalized Random Forests

## Documentation for package ‘grf’ version 1.2.0

- DESCRIPTION file.

## Help Pages

average_late | Estimate the average (conditional) local average treatment effect using a causal forest.
average_partial_effect | Estimate average partial effects using a causal forest
average_treatment_effect | Estimate average treatment effects using a causal forest
best_linear_projection | Estimate the best linear projection of a conditional average treatment effect using a causal forest.
boosted_regression_forest | Boosted regression forest (experimental)
causal_forest | Causal forest
custom_forest | Custom forest
get_sample_weights | Given a trained forest and test data, compute the training sample weights for each test point.
get_tree | Retrieve a single tree from a trained forest object.
grf | GRF
instrumental_forest | Intrumental forest
leaf_stats.causal_forest | Calculate summary stats given a set of samples for causal forests.
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.
leaf_stats.regression_forest | Calculate summary stats given a set of samples for regression forests.
ll_regression_forest | Local Linear forest
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.
predict.boosted_regression_forest | Predict with a boosted regression forest.
predict.causal_forest | Predict with a causal forest
predict.custom_forest | Predict with a custom forest.
predict.instrumental_forest | Predict with an instrumental forest
predict.ll_regression_forest | Predict with a local linear forest
predict.quantile_forest | Predict with a quantile forest
predict.regression_forest | Predict with a regression forest
predict.survival_forest | Predict with a survival forest forest
print.boosted_regression_forest | Print a boosted regression forest
print.grf | Print a GRF forest object.
print.grf_tree | Print a GRF tree object.
print.tuning_output | Print tuning output. Displays average error for q-quantiles of tuned parameters.
quantile_forest | Quantile forest
regression_forest | Regression forest
split_frequencies | Calculate which features the forest split on at each depth.
survival_forest | Survival forest
test_calibration | Omnibus evaluation of the quality of the random forest estimates via calibration.
tune_causal_forest | Causal forest tuning
tune_forest | Tune a forests
tune_instrumental_forest | Instrumental forest tuning
tune_ll_causal_forest | Local linear forest tuning
tune_ll_regression_forest | Local linear forest tuning
tune_regression_forest | Regression forest tuning
variable_importance | Calculate a simple measure of 'importance' for each feature.