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
categorical_reward_estimator
                        Learner for conducting reward estimation with
                        categorical treatments
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.
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                     Fits a model to the training data
fit_cv                  Fits a grid search to the training data with
                        cross-validation
fit_predict             Fit a reward estimation model on features,
                        treatments and outcomes and return predicted
                        counterfactual rewards for each observation, as
                        well as the score of the internal outcome
                        estimator.
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
                        Return the predicted label at a node of a tree
get_classification_proba
                        Return the predicted probabilities of class
                        membership at a node of a tree
get_depth               Get the depth of a node of a tree
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_num_fits            Return the number of fits along the path in the
                        trained 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_rank
                        Return the treatments ordered from most
                        effective to least effective at a node of a
                        tree
get_prediction_constant
                        Return the constant term in the prediction in
                        the trained learner
get_prediction_weights
                        Return the weights for numeric and categoric
                        features used for prediction in the trained
                        learner
get_prescription_treatment_rank
                        Return the treatments ordered from most
                        effective to least effective at a node of a
                        tree
get_regression_constant
                        Return the constant term in the regression
                        prediction at a node of a tree
get_regression_weights
                        Return the weights for each feature in the
                        regression prediction at a node of a 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
                        (as returned by '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_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' 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_upper_child         Get the index of the upper child at a split
                        node of a tree
glmnetcv_regressor      Learner for training GLMNet models for
                        regression problems
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
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 using multiple tree
                        learners
multi_questionnaire.default
                        Construct an interactive questionnaire using
                        multiple tree learners as specified by
                        questions
multi_questionnaire.grid_search
                        Construct an interactive tree questionnaire
                        using multiple tree 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_reward_estimator
                        Learner for conducting reward estimation with
                        numeric treatments
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_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
predict                 Return the predictions made by the model for
                        each point in the features
predict_expected_survival_time
                        Return the expected survival time estimate made
                        by a model for each point in the features.
predict_hazard          Return the fitted hazard coefficient estimate
                        made by a model for each point in the features.
predict_outcomes        Return the predicted outcome for each treatment
                        made by a model for each point in the features
predict_proba           Return the probabilities of class membership
                        predicted by a model for each point in the
                        features
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_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
questionnaire           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
read_json               Read in a learner or grid saved in JSON format
reset_display_label     Reset the predicted probability displayed to be
                        that of the predicted label when visualizing a
                        learner
reward_estimator        Learner for conducting reward estimation with
                        categorical treatments
roc_curve               Generic function for constructing an ROC curve
roc_curve.default       Construct an ROC curve from predicted
                        probabilities and true labels
roc_curve.learner       Construct an ROC curve using a trained model on
                        the given data
score                   Calculate the score for a 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_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         Show interactive visualization of an object
                        (such as a learner or curve) in the default
                        browser
show_questionnaire      Show an interactive questionnaire based on a
                        learner in default browser
single_knn_imputation_learner
                        Learner for conducting heuristic k-NN
                        imputation
split_data              Split the data into training and test datasets
transform               Impute missing values in a dataframe using a
                        fitted imputation model
tree_plot               Specify an interactive tree visualization of a
                        tree learner
variable_importance     Generate a ranking of the variables in the
                        learner according to their importance during
                        training. The results are normalized so that
                        they sum to one.
write_booster           Write the internal booster saved in the learner
                        to file
write_dot               Output a learner in .dot format
write_html              Output a 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     Output a 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
