average_late | Average LATE (removed) |
average_partial_effect | Average partial effect (removed) |
average_treatment_effect | Get doubly robust estimates of average treatment effects. |
best_linear_projection | Estimate the best linear projection of a conditional average... |
boosted_regression_forest | Boosted regression forest |
boot_grf | Simple clustered bootstrap. |
causal_forest | Causal forest |
causal_survival_forest | Causal survival forest |
create_dot_body | Writes each node information If it is a leaf node: show it in... |
custom_forest | Custom forest (removed) |
estimate_rate | Compute rate estimates, a function to be passed on to... |
expected_survival | Compute E[T | X] |
export_graphviz | Export a tree in DOT format. This function generates a... |
generate_causal_data | Generate causal forest data |
generate_causal_survival_data | Simulate causal survival data |
get_forest_weights | Given a trained forest and test data, compute the kernel... |
get_leaf_node | Find the leaf node for a test sample. |
get_sample_weights | Retrieve forest weights (renamed to get_forest_weights) |
get_scores | Compute doubly robust scores for a GRF forest object |
get_scores.causal_forest | Compute doubly robust scores for a causal forest. |
get_scores.causal_survival_forest | Compute doubly robust scores for a causal survival forest. |
get_scores.instrumental_forest | Doubly robust scores for estimating the average conditional... |
get_scores.multi_arm_causal_forest | Compute doubly robust scores for a multi arm causal forest. |
get_tree | Retrieve a single tree from a trained forest object. |
grf-package | grf: Generalized Random Forests |
instrumental_forest | Intrumental forest |
leaf_stats.causal_forest | Calculate summary stats given a set of samples for causal... |
leaf_stats.default | A default leaf_stats for forests classes without a leaf_stats... |
leaf_stats.instrumental_forest | Calculate summary stats given a set of samples for... |
leaf_stats.regression_forest | Calculate summary stats given a set of samples for regression... |
ll_regression_forest | Local linear forest |
lm_forest | LM Forest |
merge_forests | Merges a list of forests that were grown using the same data... |
multi_arm_causal_forest | Multi-arm/multi-outcome causal forest |
multi_regression_forest | Multi-task regression forest |
plot.grf_tree | Plot a GRF tree object. |
plot.rank_average_treatment_effect | Plot the Targeting Operator Characteristic curve. |
predict.boosted_regression_forest | Predict with a boosted regression forest. |
predict.causal_forest | Predict with a causal forest |
predict.causal_survival_forest | Predict with a causal survival forest forest |
predict.instrumental_forest | Predict with an instrumental forest |
predict.ll_regression_forest | Predict with a local linear forest |
predict.lm_forest | Predict with a lm forest |
predict.multi_arm_causal_forest | Predict with a multi arm causal forest |
predict.multi_regression_forest | Predict with a multi regression forest |
predict.probability_forest | Predict with a probability 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 |
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.rank_average_treatment_effect | Print the Rank-Weighted Average Treatment Effect (RATE). |
print.tuning_output | Print tuning output. Displays average error for q-quantiles... |
probability_forest | Probability forest |
quantile_forest | Quantile forest |
rank_average_treatment_effect | Estimate a Rank-Weighted Average Treatment Effect (RATE). |
rank_average_treatment_effect.fit | Fitter function for Rank-Weighted Average Treatment Effect... |
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... |
tune_causal_forest | Causal forest tuning (removed) |
tune_forest | Tune a forest |
tune_instrumental_forest | Instrumental forest tuning (removed) |
tune_ll_causal_forest | Local linear forest tuning |
tune_ll_regression_forest | Local linear forest tuning |
tune_regression_forest | Regression forest tuning (removed) |
variable_importance | Calculate a simple measure of 'importance' for each feature. |
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