'Logit model score (gradient) vector of the log-likelihood
Parameters
params: array-like
The parameters of the model
Returns
score : ndarray, 1-D
The score vector of the model, i.e. the first derivative of the
loglikelihood function, evaluated at `params`
Notes
.. math:: \frac{\partial\ln L}{\partial\beta}=\sum_{i=1}^{n}\left(y_{i}-\Lambda_{i}\right)x_{i}'

def score(self, params):

    y = self.endog
    X = self.exog
    L = self.cdf(np.dot(X, params))
    return np.dot((y - L), X)
