def calc_percentile_interval(self, conf_percentage):
        """
        Calculates percentile bootstrap confidence intervals for one's model.

        Parameters
        ----------
        conf_percentage : scalar in the interval (0.0, 100.0).
            Denotes the confidence-level for the returned endpoints. For
            instance, to calculate a 95% confidence interval, pass `95`.

        Returns
        -------
        None. Will store the percentile intervals as `self.percentile_interval`

        Notes
        -----
        Must have all ready called `self.generate_bootstrap_replicates`.
        """
        # Get the alpha % that corresponds to the given confidence percentage.
        alpha = bc.get_alpha_from_conf_percentage(conf_percentage)
        # Create the column names for the dataframe of confidence intervals
        single_column_names =\
            ['{:.3g}%'.format(alpha / 2.0),
             '{:.3g}%'.format(100 - alpha / 2.0)]
        # Calculate the desired confidence intervals.
        conf_intervals =\
            bc.calc_percentile_interval(self.bootstrap_replicates.values,
                                        conf_percentage)
        # Store the desired confidence intervals
        self.percentile_interval =\
            pd.DataFrame(conf_intervals.T,
                         index=self.mle_params.index,
                         columns=single_column_names)
        return None
Calculates percentile bootstrap confidence intervals for one's model.

        Parameters
        ----------
        conf_percentage : scalar in the interval (0.0, 100.0).
            Denotes the confidence-level for the returned endpoints. For
            instance, to calculate a 95% confidence interval, pass `95`.

        Returns
        -------
        None. Will store the percentile intervals as `self.percentile_interval`

        Notes
        -----
        Must have all ready called `self.generate_bootstrap_replicates`.