def score(self, params):
    '\n        Returns the score vector of the log-likelihood.\n\n        http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993\n\n        Parameters\n        ----------\n        params : ndarray\n            Parameter at which score is evaluated.\n\n        Returns\n        -------\n        score : ndarray\n            First derivative of loglikelihood function.\n        '
    (sf1, sf2) = self.score_factor(params)
    d1 = np.dot(sf1, self.exog)
    d2 = np.dot(sf2, self.exog_precision)
    return np.concatenate((d1, d2))
Returns the score vector of the log-likelihood.

http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993

Parameters
----------
params : ndarray
    Parameter at which score is evaluated.

Returns
-------
score : ndarray
    First derivative of loglikelihood function.
def score(self, params):
    '\n        Returns the score vector of the log-likelihood.\n\n        http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993\n\n        Parameters\n        ----------\n        params : ndarray\n            Parameter at which score is evaluated.\n\n        Returns\n        -------\n        score : ndarray\n            First derivative of loglikelihood function.\n        '
    (sf1, sf2) = self.score_factor(params)
    d1 = np.dot(sf1, self.exog)
    d2 = np.dot(sf2, self.exog_precision)
    return np.concatenate((d1, d2))