acquire_license         Acquire an IAI license for the current session.
add_julia_processes     Add additional Julia worker processes to
                        parallelize workloads
all_treatment_combinations
                        Return a dataframe containing all treatment
                        combinations of one or more treatment vectors,
                        ready for use as treatment candidates in
                        'fit_predict!' or 'predict'
apply                   Return the leaf index in a tree model into
                        which each point in the features falls
apply_nodes             Return the indices of the points in the
                        features that fall into each node of a trained
                        tree model
as.mixeddata            Convert a vector of values to IAI mixed data
                        format
autoplot.grid_search    Construct a 'ggplot2::ggplot' object plotting
                        grid search results for Optimal Feature
                        Selection learners
autoplot.roc_curve      Construct a 'ggplot2::ggplot' object plotting
                        the ROC curve
autoplot.similarity_comparison
                        Construct a 'ggplot2::ggplot' object plotting
                        the results of the similarity comparison
autoplot.stability_analysis
                        Construct a 'ggplot2::ggplot' object plotting
                        the results of the stability analysis
categorical_classification_reward_estimator
                        Learner for conducting reward estimation with
                        categorical treatments and classification
                        outcomes
categorical_regression_reward_estimator
                        Learner for conducting reward estimation with
                        categorical treatments and regression outcomes
categorical_reward_estimator
                        Learner for conducting reward estimation with
                        categorical treatments
categorical_survival_reward_estimator
                        Learner for conducting reward estimation with
                        categorical treatments and survival outcomes
cleanup_installation    Remove all traces of automatic Julia/IAI
                        installation
clone                   Return an unfitted copy of a learner with the
                        same parameters
convert_treatments_to_numeric
                        Convert 'treatments' from symbol/string format
                        into numeric values.
copy_splits_and_refit_leaves
                        Copy the tree split structure from one learner
                        into another and refit the models in each leaf
                        of the tree using the supplied data
decision_path           Return a matrix where entry '(i, j)' is true if
                        the 'i'th point in the features passes through
                        the 'j'th node in a trained tree model.
delete_rich_output_param
                        Delete a global rich output parameter
equal_propensity_estimator
                        Learner that estimates equal propensity for all
                        treatments.
fit                     Generic function for fitting a learner.
fit.grid_search         Fits a 'grid_search' to the training data
fit.imputation_learner
                        Fits an imputation learner to the training
                        data.
fit.learner             Fits a model to the training data
fit.optimal_feature_selection_learner
                        Fits an Optimal Feature Selection learner to
                        the training data
fit_and_expand          Fit an imputation learner with training
                        features and create adaptive indicator features
                        to encode the missing pattern
fit_cv                  Fits a grid search to the training data with
                        cross-validation
fit_predict             Generic function for fitting a reward estimator
                        on features, treatments and returning predicted
                        counterfactual rewards and scores of the
                        internal estimators.
fit_predict.categorical_reward_estimator
                        Fit a categorical reward estimator on features,
                        treatments and outcomes and return predicted
                        counterfactual rewards for each observation,
                        under each treatment observed in the data, as
                        well as the scores of the internal estimators.
fit_predict.numeric_reward_estimator
                        Fit a numeric reward estimator on features,
                        treatments and outcomes and return predicted
                        counterfactual rewards for each observation,
                        under each treatment candidate, as well as the
                        scores of the internal estimators.
fit_transform           Fit an imputation model using the given
                        features and impute the missing values in these
                        features
fit_transform_cv        Train a grid using cross-validation with
                        features and impute all missing values in these
                        features
get_best_params         Return the best parameter combination from a
                        grid
get_classification_label
                        Generic function for returning the predicted
                        label in the node of a classification tree
get_classification_label.classification_tree_learner
                        Return the predicted label at a node of a tree
get_classification_label.classification_tree_multi_learner
                        Return the predicted label at a node of a
                        multi-task tree
get_classification_proba
                        Generic function for returning the
                        probabilities of class membership at a node of
                        a classification tree
get_classification_proba.classification_tree_learner
                        Return the predicted probabilities of class
                        membership at a node of a tree
get_classification_proba.classification_tree_multi_learner
                        Return the predicted probabilities of class
                        membership at a node of a multi-task tree
get_cluster_assignments
                        Return the indices of the trees assigned to
                        each cluster, under the clustering of a given
                        number of trees
get_cluster_details     Return the centroid information for each
                        cluster, under the clustering of a given number
                        of trees
get_cluster_distances   Return the distances between the centroids of
                        each pair of clusters, under the clustering of
                        a given number of trees
get_depth               Get the depth of a node of a tree
get_estimation_densities
                        Return the total kernel density surrounding
                        each treatment candidate for the
                        propensity/outcome estimation problems in a
                        fitted learner.
get_features_used       Return the names of the features used by the
                        learner
get_grid_result_details
                        Return a vector of lists detailing the results
                        of the grid search
get_grid_result_summary
                        Return a summary of the results from the grid
                        search
get_grid_results        Return a summary of the results from the grid
                        search
get_learner             Return the fitted learner using the best
                        parameter combination from a grid
get_lower_child         Get the index of the lower child at a split
                        node of a tree
get_machine_id          Return the machine ID for the current computer.
get_num_fits            Generic function for returning the number of
                        fits in a trained learner
get_num_fits.glmnetcv_learner
                        Return the number of fits along the path in a
                        trained GLMNet learner
get_num_fits.optimal_feature_selection_learner
                        Return the number of fits along the path in a
                        trained Optimal Feature Selection learner
get_num_nodes           Return the number of nodes in a trained learner
get_num_samples         Get the number of training points contained in
                        a node of a tree
get_params              Return the value of all parameters on a learner
get_parent              Get the index of the parent node at a node of a
                        tree
get_policy_treatment_outcome
                        Return the quality of the treatments at a node
                        of a tree
get_policy_treatment_outcome_standard_error
                        Return the standard error for the quality of
                        the treatments at a node of a tree
get_policy_treatment_rank
                        Return the treatments ordered from most
                        effective to least effective at a node of a
                        tree
get_prediction_constant
                        Generic function for returning the prediction
                        constant in a trained learner
get_prediction_constant.glmnetcv_learner
                        Return the constant term in the prediction in a
                        trained GLMNet learner
get_prediction_constant.optimal_feature_selection_learner
                        Return the constant term in the prediction in a
                        trained Optimal Feature Selection learner
get_prediction_weights
                        Generic function for returning the prediction
                        weights in a trained learner
get_prediction_weights.glmnetcv_learner
                        Return the weights for numeric and categoric
                        features used for prediction in a trained
                        GLMNet learner
get_prediction_weights.optimal_feature_selection_learner
                        Return the weights for numeric and categoric
                        features used for prediction in a trained
                        Optimal Feature Selection learner
get_prescription_treatment_rank
                        Return the treatments ordered from most
                        effective to least effective at a node of a
                        tree
get_regression_constant
                        Generic function for returning the constant
                        term in the regression prediction at a node of
                        a tree
get_regression_constant.classification_tree_learner
                        Return the constant term in the logistic
                        regression prediction at a node of a
                        classification tree
get_regression_constant.classification_tree_multi_learner
                        Return the constant term in the logistic
                        regression prediction at a node of a multi-task
                        classification tree
get_regression_constant.prescription_tree_learner
                        Return the constant term in the linear
                        regression prediction at a node of a
                        prescription tree
get_regression_constant.regression_tree_learner
                        Return the constant term in the linear
                        regression prediction at a node of a regression
                        tree
get_regression_constant.regression_tree_multi_learner
                        Return the constant term in the linear
                        regression prediction at a node of a multi-task
                        regression tree
get_regression_constant.survival_tree_learner
                        Return the constant term in the cox regression
                        prediction at a node of a survival tree
get_regression_weights
                        Generic function for returning the weights for
                        each feature in the regression prediction at a
                        node of a tree
get_regression_weights.classification_tree_learner
                        Return the weights for each feature in the
                        logistic regression prediction at a node of a
                        classification tree
get_regression_weights.classification_tree_multi_learner
                        Return the weights for each feature in the
                        logistic regression prediction at a node of a
                        multi-task classification tree
get_regression_weights.prescription_tree_learner
                        Return the weights for each feature in the
                        linear regression prediction at a node of a
                        prescription tree
get_regression_weights.regression_tree_learner
                        Return the weights for each feature in the
                        linear regression prediction at a node of a
                        regression tree
get_regression_weights.regression_tree_multi_learner
                        Return the weights for each feature in the
                        linear regression prediction at a node of a
                        multi-task regression tree
get_regression_weights.survival_tree_learner
                        Return the weights for each feature in the cox
                        regression prediction at a node of a survival
                        tree
get_rich_output_params
                        Return the current global rich output parameter
                        settings
get_roc_curve_data      Extract the underlying data from an ROC curve
get_split_categories    Return the categoric/ordinal information used
                        in the split at a node of a tree
get_split_feature       Return the feature used in the split at a node
                        of a tree
get_split_threshold     Return the threshold used in the split at a
                        node of a tree
get_split_weights       Return the weights for numeric and categoric
                        features used in the hyperplane split at a node
                        of a tree
get_stability_results   Return the trained trees in order of increasing
                        objective value, along with their variable
                        importance scores for each feature
get_survival_curve      Return the survival curve at a node of a tree
get_survival_curve_data
                        Extract the underlying data from a survival
                        curve (as returned by
                        'predict.survival_learner' or
                        'get_survival_curve')
get_survival_expected_time
                        Return the predicted expected survival time at
                        a node of a tree
get_survival_hazard     Return the predicted hazard ratio at a node of
                        a tree
get_train_errors        Extract the training objective value for each
                        candidate tree in the comparison, where a lower
                        value indicates a better solution
get_tree                Return a copy of the learner that uses a
                        specific tree rather than the tree with the
                        best training objective.
get_upper_child         Get the index of the upper child at a split
                        node of a tree
glmnetcv_classifier     Learner for training GLMNet models for
                        classification problems with cross-validation
glmnetcv_regressor      Learner for training GLMNet models for
                        regression problems with cross-validation
glmnetcv_survival_learner
                        Learner for training GLMNet models for survival
                        problems with cross-validation
grid_search             Controls grid search over parameter
                        combinations
iai_setup               Initialize Julia and the IAI package.
imputation_learner      Generic learner for imputing missing values
impute                  Impute missing values using either a specified
                        method or through validation
impute_cv               Impute missing values using cross validation
install_julia           Download and install Julia automatically.
install_system_image    Download and install the IAI system image
                        automatically.
is_categoric_split      Check if a node of a tree applies a categoric
                        split
is_hyperplane_split     Check if a node of a tree applies a hyperplane
                        split
is_leaf                 Check if a node of a tree is a leaf
is_mixed_ordinal_split
                        Check if a node of a tree applies a mixed
                        ordinal/categoric split
is_mixed_parallel_split
                        Check if a node of a tree applies a mixed
                        parallel/categoric split
is_ordinal_split        Check if a node of a tree applies a ordinal
                        split
is_parallel_split       Check if a node of a tree applies a parallel
                        split
load_graphviz           Loads the Julia Graphviz library to permit
                        certain visualizations.
mean_imputation_learner
                        Learner for conducting mean imputation
missing_goes_lower      Check if points with missing values go to the
                        lower child at a split node of of a tree
multi_questionnaire     Generic function for constructing an
                        interactive questionnaire with multiple
                        learners
multi_questionnaire.default
                        Construct an interactive questionnaire from
                        multiple specified learners
multi_questionnaire.grid_search
                        Construct an interactive tree questionnaire
                        using multiple learners from the results of a
                        grid search
multi_tree_plot         Generic function for constructing an
                        interactive tree visualization of multiple tree
                        learners
multi_tree_plot.default
                        Construct an interactive tree visualization of
                        multiple tree learners as specified by
                        questions
multi_tree_plot.grid_search
                        Construct an interactive tree visualization of
                        multiple tree learners from the results of a
                        grid search
numeric_classification_reward_estimator
                        Learner for conducting reward estimation with
                        numeric treatments and classification outcomes
numeric_regression_reward_estimator
                        Learner for conducting reward estimation with
                        numeric treatments and regression outcomes
numeric_reward_estimator
                        Learner for conducting reward estimation with
                        numeric treatments
numeric_survival_reward_estimator
                        Learner for conducting reward estimation with
                        numeric treatments and survival outcomes
opt_knn_imputation_learner
                        Learner for conducting optimal k-NN imputation
opt_svm_imputation_learner
                        Learner for conducting optimal SVM imputation
opt_tree_imputation_learner
                        Learner for conducting optimal tree-based
                        imputation
optimal_feature_selection_classifier
                        Learner for conducting Optimal Feature
                        Selection on classification problems
optimal_feature_selection_regressor
                        Learner for conducting Optimal Feature
                        Selection on regression problems
optimal_tree_classifier
                        Learner for training Optimal Classification
                        Trees
optimal_tree_multi_classifier
                        Learner for training multi-task Optimal
                        Classification Trees
optimal_tree_multi_regressor
                        Learner for training multi-task Optimal
                        Regression Trees
optimal_tree_policy_maximizer
                        Learner for training Optimal Policy Trees where
                        the policy should aim to maximize outcomes
optimal_tree_policy_minimizer
                        Learner for training Optimal Policy Trees where
                        the policy should aim to minimize outcomes
optimal_tree_prescription_maximizer
                        Learner for training Optimal Prescriptive Trees
                        where the prescriptions should aim to maximize
                        outcomes
optimal_tree_prescription_minimizer
                        Learner for training Optimal Prescriptive Trees
                        where the prescriptions should aim to minimize
                        outcomes
optimal_tree_regressor
                        Learner for training Optimal Regression Trees
optimal_tree_survival_learner
                        Learner for training Optimal Survival Trees
optimal_tree_survivor   Learner for training Optimal Survival Trees
plot.grid_search        Plot a grid search results for Optimal Feature
                        Selection learners
plot.roc_curve          Plot an ROC curve
plot.similarity_comparison
                        Plot a similarity comparison
plot.stability_analysis
                        Plot a stability analysis
predict                 Generic function for returning the predictions
                        of a model
predict.categorical_reward_estimator
                        Return counterfactual rewards estimated by a
                        categorical reward estimator for each
                        observation in the supplied data
predict.glmnetcv_learner
                        Return the predictions made by a GLMNet learner
                        for each point in the features
predict.numeric_reward_estimator
                        Return counterfactual rewards estimated by a
                        numeric reward estimator for each observation
                        in the supplied data
predict.optimal_feature_selection_learner
                        Return the predictions made by an Optimal
                        Feature Selection learner for each point in the
                        features
predict.supervised_learner
                        Return the predictions made by a supervised
                        learner for each point in the features
predict.supervised_multi_learner
                        Return the predictions made by a multi-task
                        supervised learner for each point in the
                        features
predict.survival_learner
                        Return the predictions made by a survival
                        learner for each point in the features
predict_expected_survival_time
                        Generic function for returning the expected
                        survival time predicted by a model
predict_expected_survival_time.glmnetcv_survival_learner
                        Return the expected survival time estimate made
                        by a 'glmnetcv_survival_learner' for each point
                        in the features.
predict_expected_survival_time.survival_curve
                        Return the expected survival time estimate made
                        by a survival curve (as returned by
                        'predict.survival_learner' or
                        'get_survival_curve')
predict_expected_survival_time.survival_learner
                        Return the expected survival time estimate made
                        by a survival learner for each point in the
                        features.
predict_hazard          Generic function for returning the hazard
                        coefficient predicted by a model
predict_hazard.glmnetcv_survival_learner
                        Return the fitted hazard coefficient estimate
                        made by a 'glmnetcv_survival_learner' for each
                        point in the features.
predict_hazard.survival_learner
                        Return the fitted hazard coefficient estimate
                        made by a survival learner for each point in
                        the features.
predict_outcomes        Generic function for returning the outcomes
                        predicted by a model under each treatment
predict_outcomes.policy_learner
                        Return the predicted outcome for each treatment
                        made by a policy learner for each point in the
                        features
predict_outcomes.prescription_learner
                        Return the predicted outcome for each treatment
                        made by a prescription learner for each point
                        in the features
predict_proba           Generic function for returning the
                        probabilities of class membership predicted by
                        a model
predict_proba.classification_learner
                        Return the probabilities of class membership
                        predicted by a classification learner for each
                        point in the features
predict_proba.classification_multi_learner
                        Return the probabilities of class membership
                        predicted by a multi-task classification
                        learner for each point in the features
predict_proba.glmnetcv_classifier
                        Return the probabilities of class membership
                        predicted by a 'glmnetcv_classifier' learner
                        for each point in the features
predict_reward          Generic function for returning the
                        counterfactual rewards estimated by a model
                        under each treatment
predict_reward.categorical_reward_estimator
                        Return counterfactual rewards estimated by a
                        categorical reward estimator for each
                        observation in the supplied data and
                        predictions
predict_reward.numeric_reward_estimator
                        Return counterfactual rewards estimated by a
                        numeric reward estimator for each observation
                        in the supplied data and predictions
predict_shap            Calculate SHAP values for all points in the
                        features using the learner
predict_treatment_outcome
                        Return the estimated quality of each treatment
                        in the trained model of the learner for each
                        point in the features
predict_treatment_outcome_standard_error
                        Return the standard error for the estimated
                        quality of each treatment in the trained model
                        of the learner for each point in the features
predict_treatment_rank
                        Return the treatments in ranked order of
                        effectiveness for each point in the features
print_path              Print the decision path through the learner for
                        each sample in the features
prune_trees             Use the trained trees in a learner along with
                        the supplied validation data to determine the
                        best value for the 'cp' parameter and then
                        prune the trees according to this value
questionnaire           Generic function for constructing an
                        interactive questionnaire
questionnaire.optimal_feature_selection_learner
                        Specify an interactive questionnaire of an
                        Optimal Feature Selection learner
questionnaire.tree_learner
                        Specify an interactive questionnaire of a tree
                        learner
rand_imputation_learner
                        Learner for conducting random imputation
random_forest_classifier
                        Learner for training random forests for
                        classification problems
random_forest_regressor
                        Learner for training random forests for
                        regression problems
random_forest_survival_learner
                        Learner for training random forests for
                        survival problems
read_json               Read in a learner or grid saved in JSON format
refit_leaves            Refit the models in the leaves of a trained
                        learner using the supplied data
release_license         Release any IAI license held by the current
                        session.
reset_display_label     Reset the predicted probability displayed to be
                        that of the predicted label when visualizing a
                        learner
resume_from_checkpoint
                        Resume training from a checkpoint file
reward_estimator        Learner for conducting reward estimation with
                        categorical treatments
roc_curve               Generic function for constructing an ROC curve
roc_curve.classification_learner
                        Construct an ROC curve using a trained
                        classification learner on the given data
roc_curve.classification_multi_learner
                        Construct an ROC curve using a trained
                        multi-task classification learner on the given
                        data
roc_curve.default       Construct an ROC curve from predicted
                        probabilities and true labels
roc_curve.glmnetcv_classifier
                        Construct an ROC curve using a trained
                        'glmnetcv_classifier' on the given data
score                   Generic function for calculating scores
score.categorical_reward_estimator
                        Calculate the scores for a categorical reward
                        estimator on the given data
score.default           Calculate the score for a set of predictions on
                        the given data
score.glmnetcv_learner
                        Calculate the score for a GLMNet learner on the
                        given data
score.numeric_reward_estimator
                        Calculate the scores for a numeric reward
                        estimator on the given data
score.optimal_feature_selection_learner
                        Calculate the score for an Optimal Feature
                        Selection learner on the given data
score.supervised_learner
                        Calculate the score for a model on the given
                        data
score.supervised_multi_learner
                        Calculate the score for a multi-task model on
                        the given data
set_display_label       Show the probability of a specified label when
                        visualizing a learner
set_julia_seed          Set the random seed in Julia
set_params              Set all supplied parameters on a learner
set_reward_kernel_bandwidth
                        Save a new reward kernel bandwidth inside a
                        learner, and return new reward predictions
                        generated using this bandwidth for the original
                        data used to train the learner.
set_rich_output_param   Sets a global rich output parameter
set_threshold           For a binary classification problem, update the
                        the predicted labels in the leaves of the
                        learner to predict a label only if the
                        predicted probability is at least the specified
                        threshold.
show_in_browser         Generic function for showing interactive
                        visualization in browser
show_in_browser.abstract_visualization
                        Show interactive visualization of an object in
                        the default browser
show_in_browser.roc_curve
                        Show interactive visualization of a 'roc_curve'
                        in the default browser
show_in_browser.tree_learner
                        Show interactive tree visualization of a tree
                        learner in the default browser
show_questionnaire      Generic function for showing interactive
                        questionnaire in browser
show_questionnaire.optimal_feature_selection_learner
                        Show an interactive questionnaire based on an
                        Optimal Feature Selection learner in default
                        browser
show_questionnaire.tree_learner
                        Show an interactive questionnaire based on a
                        tree learner in default browser
similarity_comparison   Conduct a similarity comparison between the
                        final tree in a learner and all trees in a new
                        learner to consider the tradeoff between
                        training performance and similarity to the
                        original tree
single_knn_imputation_learner
                        Learner for conducting heuristic k-NN
                        imputation
split_data              Split the data into training and test datasets
stability_analysis      Conduct a stability analysis of the trees in a
                        tree learner
transform               Impute missing values in a dataframe using a
                        fitted imputation model
transform_and_expand    Transform features with a trained imputation
                        learner and create adaptive indicator features
                        to encode the missing pattern
tree_plot               Specify an interactive tree visualization of a
                        tree learner
tune_reward_kernel_bandwidth
                        Conduct the reward kernel bandwidth tuning
                        procedure for a range of starting bandwidths
                        and return the final tuned values.
variable_importance     Generic function for calculating variable
                        importance
variable_importance.learner
                        Generate a ranking of the variables in a
                        learner according to their importance during
                        training. The results are normalized so that
                        they sum to one.
variable_importance.optimal_feature_selection_learner
                        Generate a ranking of the variables in an
                        Optimal Feature Selection learner according to
                        their importance during training. The results
                        are normalized so that they sum to one.
variable_importance.tree_learner
                        Generate a ranking of the variables in a tree
                        learner according to their importance during
                        training. The results are normalized so that
                        they sum to one.
variable_importance_similarity
                        Calculate similarity between the final tree in
                        a tree learner with all trees in new tree
                        learner using variable importance scores.
write_booster           Write the internal booster saved in the learner
                        to file
write_dot               Output a learner in .dot format
write_html              Generic function for writing interactive
                        visualization to file
write_html.abstract_visualization
                        Output an object as an interactive browser
                        visualization in HTML format
write_html.roc_curve    Output an ROC curve as an interactive browser
                        visualization in HTML format
write_html.tree_learner
                        Output a tree learner as an interactive browser
                        visualization in HTML format
write_json              Output a learner or grid in JSON format
write_pdf               Output a learner as a PDF image
write_png               Output a learner as a PNG image
write_questionnaire     Generic function for writing interactive
                        questionnaire to file
write_questionnaire.optimal_feature_selection_learner
                        Output an Optimal Feature Selection learner as
                        an interactive questionnaire in HTML format
write_questionnaire.tree_learner
                        Output a tree learner as an interactive
                        questionnaire in HTML format
write_svg               Output a learner as a SVG image
xgboost_classifier      Learner for training XGBoost models for
                        classification problems
xgboost_regressor       Learner for training XGBoost models for
                        regression problems
xgboost_survival_learner
                        Learner for training XGBoost models for
                        survival problems
zero_imputation_learner
                        Learner for conducting zero-imputation
