def loglike(self, endog, mu, freq_weights=1., scale=1.):
        r"""
        The log-likelihood function in terms of the fitted mean response.

        Parameters
        ----------
        endog : array-like
            Endogenous response variable
        mu : array-like
            Fitted mean response variable
        freq_weights : array-like
            1d array of frequency weights. The default is 1.
        scale : float, optional
            The scale parameter, defaults to 1.

        Returns
        -------
        llf : float
            The value of the loglikelihood function evaluated at
            (endog,mu,freq_weights,scale) as defined below.

        """
        loglike = np.sum(freq_weights * (endog * np.log(mu) - mu -
                         special.gammaln(endog + 1)))
        return scale * loglike
def loglike ( self , endog , mu , freq_weights = 1. , scale = 1. ) : loglike = np . sum ( freq_weights * ( endog * np . log ( mu ) - mu - special . gammaln ( endog + 1 ) ) ) return scale * loglike
r"""
        The log-likelihood function in terms of the fitted mean response.

        Parameters
        ----------
        endog : array-like
            Endogenous response variable
        mu : array-like
            Fitted mean response variable
        freq_weights : array-like
            1d array of frequency weights. The default is 1.
        scale : float, optional
            The scale parameter, defaults to 1.

        Returns
        -------
        llf : float
            The value of the loglikelihood function evaluated at
            (endog,mu,freq_weights,scale) as defined below.