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| 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 |
| 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. |
| 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. |