docs: 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))