code: def mu(self, lp, dist):
          
          elp = np.exp(lp)
          return dist.levels * elp / (elp + 1)
docs: glm mean function, ie inverse of link function
          this is useful for going from the linear prediction to mu
  
          Parameters
          ----------
          lp : array-like of legth n
          dist : Distribution instance
  
          Returns
          -------
          mu : np.array of length n