# Generalized Random Forests (Beta)

## Documentation for package ‘grf’ version 0.10.2

- DESCRIPTION file.

## Help Pages

average_partial_effect | Estimate average partial effects using a causal forest
average_treatment_effect | Estimate average treatment effects using a causal forest
causal_forest | Causal forest
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
custom_forest | Custom forest
export_graphviz | Export a tree in DOT format. This function generates a GraphViz representation of the tree, which is then written into 'dot_string'.
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
local_linear_forest | Local Linear forest
plot.grf_tree | Plot a GRF tree object.
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.local_linear_forest | Predict with a local linear forest
predict.quantile_forest | Predict with a quantile forest
predict.regression_forest | Predict with a regression forest
print.grf | Print a GRF forest object.
print.grf_tree | Print a GRF tree object.
quantile_forest | Quantile forest
regression_forest | Regression forest
split_frequencies | Calculate which features the forest split on at each depth.
test_calibration | Omnibus evaluation of the quality of the random forest estimates via calibration.
tune_causal_forest | Causal forest tuning
tune_local_linear_forest | Local linear forest tuning
tune_regression_forest | Regression forest tuning
variable_importance | Calculate a simple measure of 'importance' for each feature.