docs: def get_ci(theta_star, blockratio=1.0):
      """ Get the confidence interval. """
      # get rid of nans while we sort
      b_star = np.sort(theta_star[~np.isnan(theta_star)])
      se = np.std(b_star) * np.sqrt(blockratio)
      # bootstrap 95% CI based on empirical percentiles
      ci = [b_star[int(len(b_star) * .025)], b_star[int(len(b_star) * .975)]]
      return ci