Man pages for grf
Generalized Random Forests
| 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... |
| 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_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_options | grf package options |
| 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_forest | Tune a forest |
| tune_ll_causal_forest | Local linear forest tuning |
| tune_ll_regression_forest | Local linear forest tuning |
| variable_importance | Calculate a simple measure of 'importance' for each feature. |
