'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".'

def standard_errors(self, cov_type='robust'):

    covariance_type = cov_type.lower()
    allowed_covariances = ['robust', 'naive', 'bias_reduced']
    if (covariance_type not in allowed_covariances):
        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'):
        if (self.cov_robust_bc is None):
            raise ValueError('GEE:    `bias_reduced`    covariance    not    available')
        return np.sqrt(np.diag(self.cov_robust_bc))
