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 default is 1.

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

        Notes
        -----
        .. math::

           llf = -1/2 * \sum_i freq\_weights_i * ((Y_i - \mu_i)^2 / (Y_i *
                 \mu_i * scale) + \log(scale * Y_i^3) + \log(2 * \pi))
        """
        return -.5 * np.sum(((endog - mu)**2/(endog * mu**2 * scale) +
                             np.log(scale * endog**3) + np.log(2 * np.pi)) *
                            freq_weights)
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/genmod/families/family.py/InverseGaussian.loglike/original