bootstrap_performance   Calculate a bootstrap confidence interval for
                        the performance on a single train/test split
calc_balanced_precision
                        Calculate balanced precision given actual and
                        baseline precision
calc_baseline_precision
                        Calculate the fraction of positives, i.e.
                        baseline precision for a PRC curve
calc_mean_perf          Generic function to calculate mean performance
                        curves for multiple models
calc_model_sensspec     Calculate and summarize performance for ROC and
                        PRC plots
calc_perf_metrics       Get performance metrics for test data
combine_hp_performance
                        Combine hyperparameter performance metrics for
                        multiple train/test splits
compare_models          Perform permutation tests to compare the
                        performance metric across all pairs of a group
                        variable.
define_cv               Define cross-validation scheme and training
                        parameters
get_caret_processed_df
                        Get preprocessed dataframe for continuous
                        variables
get_feature_importance
                        Get feature importance using the permutation
                        method
get_hp_performance      Get hyperparameter performance metrics
get_hyperparams_list    Set hyperparameters based on ML method and
                        dataset characteristics
get_outcome_type        Get outcome type.
get_partition_indices   Select indices to partition the data into
                        training & testing sets.
get_perf_metric_fn      Get default performance metric function
get_perf_metric_name    Get default performance metric name
get_performance_tbl     Get model performance metrics as a one-row
                        tibble
get_tuning_grid         Generate the tuning grid for tuning
                        hyperparameters
group_correlated_features
                        Group correlated features
otu_data_preproc        Mini OTU abundance dataset - preprocessed
otu_mini_bin            Mini OTU abundance dataset
otu_mini_bin_results_glmnet
                        Results from running the pipeline with L2
                        logistic regression on 'otu_mini_bin' with
                        feature importance and grouping
otu_mini_bin_results_rf
                        Results from running the pipeline with random
                        forest on 'otu_mini_bin'
otu_mini_bin_results_rpart2
                        Results from running the pipeline with rpart2
                        on 'otu_mini_bin'
otu_mini_bin_results_svmRadial
                        Results from running the pipeline with
                        svmRadial on 'otu_mini_bin'
otu_mini_bin_results_xgbTree
                        Results from running the pipeline with xbgTree
                        on 'otu_mini_bin'
otu_mini_cont_results_glmnet
                        Results from running the pipeline with glmnet
                        on 'otu_mini_bin' with 'Otu00001' as the
                        outcome
otu_mini_cont_results_nocv
                        Results from running the pipeline with glmnet
                        on 'otu_mini_bin' with 'Otu00001' as the
                        outcome column, using a custom train control
                        scheme that does not perform cross-validation
otu_mini_cv             Cross validation on 'train_data_mini' with
                        grouped features.
otu_mini_multi          Mini OTU abundance dataset with 3 categorical
                        variables
otu_mini_multi_group    Groups for otu_mini_multi
otu_mini_multi_results_glmnet
                        Results from running the pipeline with glmnet
                        on 'otu_mini_multi' for multiclass outcomes
otu_small               Small OTU abundance dataset
permute_p_value         Calculated a permuted p-value comparing two
                        models
plot_hp_performance     Plot hyperparameter performance metrics
plot_mean_roc           Plot ROC and PRC curves
plot_model_performance
                        Plot performance metrics for multiple ML runs
                        with different parameters
preprocess_data         Preprocess data prior to running machine
                        learning
randomize_feature_order
                        Randomize feature order to eliminate any
                        position-dependent effects
remove_singleton_columns
                        Remove columns appearing in only 'threshold'
                        row(s) or fewer.
replace_spaces          Replace spaces in all elements of a character
                        vector with underscores
run_ml                  Run the machine learning pipeline
tidy_perf_data          Tidy the performance dataframe
train_model             Train model using 'caret::train()'.
