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.
