add_SAM_utility_cols    Add SAM utility columns to table
as.model_list           Make models into model_list object
build_connection_string
                        Build a connection string for use with MSSQL
                        and dbConnect
catalyst_test_deploy_in_prod
                        Test function to check that the production
                        environment is active.
control_chart           Create a control chart
convert_date_cols       Convert character date columns to dates
countMissingData        Function to find proportion of NAs in each
                        column of a dataframe or matrix
db_read                 Read from a SQL Server database table
evaluate                Get model performance metrics
evaluate_classification
                        Get performance metrics for classification
                        predictions
evaluate_regression     Get performance metrics for regression
                        predictions
flash_models            Train models without tuning for performance
get_hyperparameter_defaults
                        Get hyperparameter values
get_supported_models    Supported models and their hyperparameters
get_variable_importance
                        Get variable importances
hcai_impute             Specify imputation methods for an existing
                        recipe
healthcareai            Machine Learning Made Easy
impute                  Impute data and return a reusable recipe
is.model_list           Type checks
is.predicted_df         Class check
machine_learn           Machine learning made easy
missingness             Find missingness in each column and search for
                        strings that might represent missing values
pima_diabetes           Patient diabetes dataset
pivot                   Pivot multiple rows per observation to one row
                        with multiple columns
plot.missingness        Plot missingness
plot.model_list         Plot performance of models
plot.predicted_df       Plot model predictions vs observed outcomes
plot.variable_importance
                        Plot variable importance
predict.model_list      Make predictions using the best-performing
                        model
prep_data               Prepare data for machine learning
save_models             Save models to disk and load models from disk
selectData              Defunct. See 'db_read'
separate_drgs           Convert MSDRGs into a "base DRG" and
                        complication level
split_train_test        Split data into training and test data frames
start_prod_logs         Sets console logging to a file in the working
                        directory.
step_add_levels         Add levels to nominal variables
step_date_hcai          Date Feature Generator
step_missing            Clean NA values from categorical/nominal
                        variables
stop_prod_logs          Stops all console logging.
tune_models             Tune multiple machine learning models using
                        cross validation to optimize performance
writeData               Defunct. See this vignette for help writing to
                        databases.
