Returns the confidence interval of the fitted parameters.

        Parameters
        ----------
        alpha : float, optional
                The significance level for the confidence interval.
                ie., The default `alpha` = .05 returns a 95% confidence
                interval.
        cols  : array-like, optional
               `cols` specifies which confidence intervals to return
        method: string
                 Not Implemented Yet
                 Method to estimate the confidence_interval.
                 "Default" : uses self.bse which is based on inverse Hessian
                 for MLE.
                 "hjjh" :
                 "jac" :
                 "boot-bse"
                 "boot_quant"
                 "profile"

        Returns
        --------
        conf_int : array
                   Each row contains [lower, upper] limits of the confidence
                   interval for the corresponding parameter. The first column
                   contains all lower, the second column contains all upper
                   limits.

        Examples
        --------
        >>> import libpysal as ps
        >>> from spglm.glm import GLM
        >>> import numpy as np
        >>> db = ps.io.open(ps.examples.get_path('columbus.dbf'),'r')
        >>> y = np.array(db.by_col("HOVAL")).reshape((-1,1))
        >>> X = []
        >>> X.append(db.by_col("INC"))
        >>> X.append(db.by_col("CRIME"))
        >>> X = np.array(X).T
        >>> model = GLM(y, X)
        >>> results = model.fit()
        >>> results.conf_int()
        array([[ 20.57281401,  72.28355135],
               [ -0.42138121,   1.67934915],
               [ -0.84292086,  -0.12685622]])

        Notes
        -----
        The confidence interval is based on the standard normal distribution.
        Models wish to use a different distribution should overwrite this
        method.

def conf_int(self, alpha=.05, cols=None, method='default'):
        """
        Returns the confidence interval of the fitted parameters.

        Parameters
        ----------
        alpha : float, optional
                The significance level for the confidence interval.
                ie., The default `alpha` = .05 returns a 95% confidence
                interval.
        cols  : array-like, optional
               `cols` specifies which confidence intervals to return
        method: string
                 Not Implemented Yet
                 Method to estimate the confidence_interval.
                 "Default" : uses self.bse which is based on inverse Hessian
                 for MLE.
                 "hjjh" :
                 "jac" :
                 "boot-bse"
                 "boot_quant"
                 "profile"

        Returns
        --------
        conf_int : array
                   Each row contains [lower, upper] limits of the confidence
                   interval for the corresponding parameter. The first column
                   contains all lower, the second column contains all upper
                   limits.

        Examples
        --------
        >>> import libpysal as ps
        >>> from spglm.glm import GLM
        >>> import numpy as np
        >>> db = ps.io.open(ps.examples.get_path('columbus.dbf'),'r')
        >>> y = np.array(db.by_col("HOVAL")).reshape((-1,1))
        >>> X = []
        >>> X.append(db.by_col("INC"))
        >>> X.append(db.by_col("CRIME"))
        >>> X = np.array(X).T
        >>> model = GLM(y, X)
        >>> results = model.fit()
        >>> results.conf_int()
        array([[ 20.57281401,  72.28355135],
               [ -0.42138121,   1.67934915],
               [ -0.84292086,  -0.12685622]])

        Notes
        -----
        The confidence interval is based on the standard normal distribution.
        Models wish to use a different distribution should overwrite this
        method.
        """
        bse = self.bse

        if self.use_t:
            dist = stats.t
            df_resid = getattr(self, 'df_resid_inference', self.df_resid)
            q = dist.ppf(1 - alpha / 2, df_resid)
        else:
            dist = stats.norm
            q = dist.ppf(1 - alpha / 2)

        if cols is None:
            lower = self.params - q * bse
            upper = self.params + q * bse
        else:
            cols = np.asarray(cols)
            lower = self.params[cols] - q * bse[cols]
            upper = self.params[cols] + q * bse[cols]
        return np.asarray(lzip(lower, upper))