'Returns the confidence interval of the value, `effect` of the constraint.
This is currently only available for t and z tests.
Parameters
alpha : float, optional
The significance level for the confidence interval.
ie., The default `alpha` = .05 returns a 95% confidence interval.
kwds : extra keyword arguments
currently ignored, only for compatibility, consistent signature
Returns
ci : ndarray, (k_constraints, 2)
The array has the lower and the upper limit of the confidence
interval in the columns.'

def conf_int(self, method='endpoint', alpha=0.05, **kwds):

    tmp = np.linspace(0, 1, 6)
    is_linear = (self.link.inverse(tmp) == tmp).all()
    if ((method == 'endpoint') and (not is_linear)):
        ci_linear = self.linpred.conf_int(alpha=alpha, obs=False)
        ci = self.link.inverse(ci_linear)
    elif ((method == 'delta') or is_linear):
        se = self.se_mean
        q = self.dist.ppf((1 - (alpha / 2.0)), *self.dist_args)
        lower = (self.predicted_mean - (q * se))
        upper = (self.predicted_mean + (q * se))
        ci = np.column_stack((lower, upper))
    return ci
