def _deriv_mean_dparams(self, params):
    '\n        Derivative of the expected endog with respect to the parameters.\n\n        Parameters\n        ----------\n        params : ndarray\n            parameter at which score is evaluated\n\n        Returns\n        -------\n        The value of the derivative of the expected endog with respect\n        to the parameter vector.\n        '
    params_infl = params[:self.k_inflate]
    params_main = params[self.k_inflate:]
    w = self.model_infl.predict(params_infl)
    w = np.clip(w, np.finfo(float).eps, (1 - np.finfo(float).eps))
    mu = self.model_main.predict(params_main)
    score_infl = self.model_infl._deriv_mean_dparams(params_infl)
    score_main = self.model_main._deriv_mean_dparams(params_main)
    dmat_infl = ((- mu[:, None]) * score_infl)
    dmat_main = ((1 - w[:, None]) * score_main)
    dmat = np.column_stack((dmat_infl, dmat_main))
    return dmat
Derivative of the expected endog with respect to the parameters.

Parameters
----------
params : ndarray
    parameter at which score is evaluated

Returns
-------
The value of the derivative of the expected endog with respect
to the parameter vector.
def _deriv_mean_dparams(self, params):
    '\n        Derivative of the expected endog with respect to the parameters.\n\n        Parameters\n        ----------\n        params : ndarray\n            parameter at which score is evaluated\n\n        Returns\n        -------\n        The value of the derivative of the expected endog with respect\n        to the parameter vector.\n        '
    params_infl = params[:self.k_inflate]
    params_main = params[self.k_inflate:]
    w = self.model_infl.predict(params_infl)
    w = np.clip(w, np.finfo(float).eps, (1 - np.finfo(float).eps))
    mu = self.model_main.predict(params_main)
    score_infl = self.model_infl._deriv_mean_dparams(params_infl)
    score_main = self.model_main._deriv_mean_dparams(params_main)
    dmat_infl = ((- mu[:, None]) * score_infl)
    dmat_main = ((1 - w[:, None]) * score_main)
    dmat = np.column_stack((dmat_infl, dmat_main))
    return dmat