'Logit model Hessian matrix of the log-likelihood
Parameters
params : array-like
The parameters of the model
Returns
hess : ndarray, (k_vars, k_vars)
The Hessian, second derivative of loglikelihood function,
evaluated at `params`
Notes
.. math:: \frac{\partial^{2}\ln L}{\partial\beta\partial\beta^{\prime}}=-\sum_{i}\Lambda_{i}\left(1-\Lambda_{i}\right)x_{i}x_{i}^{\prime}'

def hessian(self, params):

    X = self.exog
    L = self.cdf(np.dot(X, params))
    return (- np.dot(((L * (1 - L)) * X.T), X))
