create_person_period_data
                        Generates person-period data for any data set,
                        given the bounds defined by the training set.
create_synthetic_data   Generates a survival data set for synthetic
                        streaming service subscription data.  The
                        survival event in this case is a cancellation
                        of the subscription. It is given as a function
                        of household income and average number of hours
                        watched in the prior month. Users can adjust
                        the level of censoring and variance in the data
                        with the supplied parameters or simply call
                        with no parameters for a default distribution
                        of data.
create_training_data    Generates modeling data from a person-period
                        data set.
evaluate_model          Generates evaluation metrics, include
                        time-dependent TPR and FPR rates as well as AUC
generate_bounds         Generates the intervals based on the survival
                        times in the supplied data set using the
                        quantile function.
plot_km                 Plots a series of population Kaplan-Meier
                        curves for different thresholds for both the
                        test predictions and the ground truth
plot_survival_curve     Plots a sample of individual survival curves
                        from the test data set.
plot_synthetic_data     Simple visualization of synthetic subscription
                        data.
spect_predict           Generates predictions for each individual at
                        each interval defined by the 'train_result'
                        parameter. The interval-level predictions can
                        be combined to generate surivival curves for an
                        individual.
spect_train             Generates a trained caret model using the given
                        primary binary classification. Optionally
                        generates a stacked ensemble model if a list of
                        base learners is supplied.
