'Logit model Jacobian of the log-likelihood for each observation
Parameters
params: array-like
The parameters of the model
Returns
jac : array-like
The derivative of the loglikelihood for each observation evaluated
at `params`.
Notes
.. math:: \frac{\partial\ln L_{i}}{\partial\beta}=\left(y_{i}-\Lambda_{i}\right)x_{i}
for observations :math:`i=1,...,n`'

def score_obs(self, params):

    y = self.endog
    X = self.exog
    L = self.cdf(np.dot(X, params))
    return ((y - L)[:, None] * X)
