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iai  

Interface to 'Interpretable AI' Modules
View on CRAN: Click here


Download and install iai package within the R console
Install from CRAN:
install.packages("iai")

Install from Github:
library("remotes")
install_github("cran/iai")

Install by package version:
library("remotes")
install_version("iai", "1.10.2")



Attach the package and use:
library("iai")
Maintained by
Jack Dunn
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-07-18
Latest Update: 2024-10-18
Description:
An interface to the algorithms of 'Interpretable AI' from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available.
How to cite:
Jack Dunn (2019). iai: Interface to 'Interpretable AI' Modules. R package version 1.10.2, https://cran.r-project.org/web/packages/iai. Accessed 06 Jun. 2026.
Previous versions and publish date:
1.0.0 (2019-07-18 08:36), 1.1.0 (2019-09-13 06:40), 1.2.0 (2020-01-29 23:20), 1.3.0 (2020-08-05 21:50), 1.4.0 (2020-12-09 15:40), 1.5.0 (2021-02-03 15:20), 1.6.0 (2021-06-09 16:30), 1.7.0 (2021-12-06 15:00), 1.8.0 (2022-08-29 22:50), 1.9.0 (2023-04-04 20:00), 1.10.0 (2023-06-13 18:10), 1.10.1 (2024-06-18 06:00)
Other packages that cited iai R package
View iai citation profile
Other R packages that iai depends, imports, suggests or enhances
Complete documentation for iai
Functions, R codes and Examples using the iai R package
Some associated 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_result_details . get_grid_result_summary . get_grid_results . 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 . opt_knn_imputation_learner . opt_svm_imputation_learner . opt_tree_imputation_learner . 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 . 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 . rand_imputation_learner . random_forest_classifier . random_forest_regressor . random_forest_survival_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 . 
Some associated R codes: generic.R . heuristics.R . iaibase.R . iaitrees.R . install.R . interface.R . interface_class.R . optimalfeatureselection.R . optimaltrees.R . optimpute.R . rewardestimation.R . utils.R .  Full iai package functions and examples
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