def standard_errors(self, cov_type="robust"):
        """
        This is a convenience function that returns the standard
        errors for any covariance type.  The value of `bse` is the
        standard errors for whichever covariance type is specified as
        an argument to `fit` (defaults to "robust").

        Parameters
        ----------
        cov_type : string
            One of "robust", "naive", or "bias_reduced".  Determines
            the covariance used to compute standard errors.  Defaults
            to "robust".
        """

        # Check covariance_type
        covariance_type = cov_type.lower()
        allowed_covariances = ["robust", "naive", "bias_reduced"]
        if allowed_covariances not in covariance_type:
            msg = "GEE: `covariance_type` must be one of " +\
                ", ".join(allowed_covariances)
            raise ValueError(msg)

        if covariance_type == "robust":
            return np.sqrt(np.diag(self.cov_robust))
        elif covariance_type == "naive":
            return np.sqrt(np.diag(self.cov_naive))
        elif covariance_type == "bias_reduced":
            return np.sqrt(np.diag(self.cov_robust_bc))

    # Need to override to allow for different covariance types.
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/genmod/generalized_estimating_equations.py/GEEResults.standard_errors/swapped operands of `not in`