R Packages that start with:
A . B . C . D . E . F . G . H . I . J . K . L . M . N . O . P . Q . R . S . T . U . V . W . X . Y . Z .
Functions
- acquire_license()
- add_julia_processes()
- all_treatment_combinations()
- apply()
- apply_nodes()
- as.mixeddata()
- autoplot.grid_search()
- autoplot.roc_curve()
- autoplot.similarity_comparison()
- autoplot.stability_analysis()
- categorical_classification_reward_estimator()
- categorical_regression_reward_estimator()
- categorical_reward_estimator()
- categorical_survival_reward_estimator()
- cleanup_installation()
- clone()
- convert_treatments_to_numeric()
- copy_splits_and_refit_leaves()
- decision_path()
- delete_rich_output_param()
- equal_propensity_estimator()
- fit.grid_search()
- fit.imputation_learner()
- fit.learner()
- fit()
- fit.optimal_feature_selection_learner()
- fit_and_expand()
- fit_cv()
- fit_predict.categorical_reward_estimator()
- fit_predict.numeric_reward_estimator()
- fit_predict()
- fit_transform()
- fit_transform_cv()
- get_best_params()
- get_classification_label.classification_tree_learner()
- get_classification_label.classification_tree_multi_learner()
- get_classification_label()
- get_classification_proba.classification_tree_learner()
- get_classification_proba.classification_tree_multi_learner()
- get_classification_proba()
- get_cluster_assignments()
- get_cluster_details()
- get_cluster_distances()
- get_depth()
- get_estimation_densities()
- get_features_used()
- get_grid_results()
- get_grid_result_details()
- get_grid_result_summary()
- get_learner()
- get_lower_child()
- get_machine_id()
- get_num_fits.glmnetcv_learner()
- get_num_fits()
- get_num_fits.optimal_feature_selection_learner()
- get_num_nodes()
- get_num_samples()
- get_params()
- get_parent()
- get_policy_treatment_outcome()
- get_policy_treatment_outcome_standard_error()
- get_policy_treatment_rank()
- get_prediction_constant.glmnetcv_learner()
- get_prediction_constant()
- get_prediction_constant.optimal_feature_selection_learner()
- get_prediction_weights.glmnetcv_learner()
- get_prediction_weights()
- get_prediction_weights.optimal_feature_selection_learner()
- get_prescription_treatment_rank()
- get_regression_constant.classification_tree_learner()
- get_regression_constant.classification_tree_multi_learner()
- get_regression_constant()
- get_regression_constant.prescription_tree_learner()
- get_regression_constant.regression_tree_learner()
- get_regression_constant.regression_tree_multi_learner()
- get_regression_constant.survival_tree_learner()
- get_regression_weights.classification_tree_learner()
- get_regression_weights.classification_tree_multi_learner()
- get_regression_weights()
- get_regression_weights.prescription_tree_learner()
- get_regression_weights.regression_tree_learner()
- get_regression_weights.regression_tree_multi_learner()
- get_regression_weights.survival_tree_learner()
- get_rich_output_params()
- get_roc_curve_data()
- get_split_categories()
- get_split_feature()
- get_split_threshold()
- get_split_weights()
- get_stability_results()
- get_survival_curve()
- get_survival_curve_data()
- get_survival_expected_time()
- get_survival_hazard()
- get_train_errors()
- get_tree()
- get_upper_child()
- glmnetcv_classifier()
- glmnetcv_regressor()
- glmnetcv_survival_learner()
- grid_search()
- iai_setup()
- imputation_learner()
- impute()
- impute_cv()
- install_julia()
- install_system_image()
- is_categoric_split()
- is_hyperplane_split()
- is_leaf()
- is_mixed_ordinal_split()
- is_mixed_parallel_split()
- is_ordinal_split()
- is_parallel_split()
- load_graphviz()
- mean_imputation_learner()
- missing_goes_lower()
- multi_questionnaire.default()
- multi_questionnaire.grid_search()
- multi_questionnaire()
- multi_tree_plot.default()
- multi_tree_plot.grid_search()
- multi_tree_plot()
- numeric_classification_reward_estimator()
- numeric_regression_reward_estimator()
- numeric_reward_estimator()
- numeric_survival_reward_estimator()
- optimal_feature_selection_classifier()
- optimal_feature_selection_regressor()
- optimal_tree_classifier()
- optimal_tree_multi_classifier()
- optimal_tree_multi_regressor()
- optimal_tree_policy_maximizer()
- optimal_tree_policy_minimizer()
- optimal_tree_prescription_maximizer()
- optimal_tree_prescription_minimizer()
- optimal_tree_regressor()
- optimal_tree_survival_learner()
- optimal_tree_survivor()
- opt_knn_imputation_learner()
- opt_svm_imputation_learner()
- opt_tree_imputation_learner()
- plot.grid_search()
- plot.roc_curve()
- plot.similarity_comparison()
- plot.stability_analysis()
- predict.categorical_reward_estimator()
- predict.glmnetcv_learner()
- predict.numeric_reward_estimator()
- predict()
- predict.optimal_feature_selection_learner()
- predict.supervised_learner()
- predict.supervised_multi_learner()
- predict.survival_learner()
- predict_expected_survival_time.glmnetcv_survival_learner()
- predict_expected_survival_time()
- predict_expected_survival_time.survival_curve()
- predict_expected_survival_time.survival_learner()
- predict_hazard.glmnetcv_survival_learner()
- predict_hazard()
- predict_hazard.survival_learner()
- predict_outcomes()
- predict_outcomes.policy_learner()
- predict_outcomes.prescription_learner()
- predict_proba.classification_learner()
- predict_proba.classification_multi_learner()
- predict_proba.glmnetcv_classifier()
- predict_proba()
- predict_reward.categorical_reward_estimator()
- predict_reward.numeric_reward_estimator()
- predict_reward()
- predict_shap()
- predict_treatment_outcome()
- predict_treatment_outcome_standard_error()
- predict_treatment_rank()
- print_path()
- prune_trees()
- questionnaire()
- questionnaire.optimal_feature_selection_learner()
- questionnaire.tree_learner()
- random_forest_classifier()
- random_forest_regressor()
- random_forest_survival_learner()
- rand_imputation_learner()
- read_json()
- refit_leaves()
- release_license()
- reset_display_label()
- resume_from_checkpoint()
- reward_estimator()
- roc_curve.classification_learner()
- roc_curve.classification_multi_learner()
- roc_curve.default()
- roc_curve.glmnetcv_classifier()
- roc_curve()
- score.categorical_reward_estimator()
- score.default()
- score.glmnetcv_learner()
- score.numeric_reward_estimator()
- score()
- score.optimal_feature_selection_learner()
- score.supervised_learner()
- score.supervised_multi_learner()
- set_display_label()
- set_julia_seed()
- set_params()
- set_reward_kernel_bandwidth()
- set_rich_output_param()
- set_threshold()
- show_in_browser.abstract_visualization()
- show_in_browser()
- show_in_browser.roc_curve()
- show_in_browser.tree_learner()
- show_questionnaire()
- show_questionnaire.optimal_feature_selection_learner()
- show_questionnaire.tree_learner()
- similarity_comparison()
- single_knn_imputation_learner()
- split_data()
- stability_analysis()
- transform()
- transform_and_expand()
- tree_plot()
- tune_reward_kernel_bandwidth()
- variable_importance.learner()
- variable_importance()
- variable_importance.optimal_feature_selection_learner()
- variable_importance.tree_learner()
- variable_importance_similarity()
- write_booster()
- write_dot()
- write_html.abstract_visualization()
- write_html()
- write_html.roc_curve()
- write_html.tree_learner()
- write_json()
- write_pdf()
- write_png()
- write_questionnaire()
- write_questionnaire.optimal_feature_selection_learner()
- write_questionnaire.tree_learner()
- write_svg()
- xgboost_classifier()
- xgboost_regressor()
- xgboost_survival_learner()
- zero_imputation_learner()
R Codes
Selected R package: iai
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