def general_neg_loglik(self, beta):
        """ Calculates the negative log-likelihood of the model

        Parameters
        ----------
        beta : np.ndarray
            Contains untransformed starting values for latent variables

        Returns
        ----------
        The negative logliklihood of the model
        """     

        mu, Y = self._model(beta)
        parm = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])
        #TODO: Replace above with transformation that only acts on scale, shape, skewness in future (speed-up)
        model_scale, model_shape, model_skewness = self._get_scale_and_shape(parm)
        return self.family.neg_loglikelihood(Y, self.link(mu), model_scale, model_shape, model_skewness)
def general_neg_loglik ( self , beta ) : mu , Y = self . _model ( beta ) parm = np . array ( [ self . latent_variables . z_list [ k ] . prior . transform ( beta [ k ] ) for k in range ( beta . shape [ 0 ] ) ] ) model_scale , model_shape , model_skewness = self . _get_scale_and_shape ( parm ) return self . family . neg_loglikelihood ( Y , self . link ( mu ) , model_scale , model_shape , model_skewness )
Calculates the negative log-likelihood of the model

        Parameters
        ----------
        beta : np.ndarray
            Contains untransformed starting values for latent variables

        Returns
        ----------
        The negative logliklihood of the model