Creates the structure of the model

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

        Returns
        ----------
        theta : np.array
            Contains the predicted values for the time series

        Y : np.array
            Contains the length-adjusted time series (accounting for lags)

        scores : np.array
            Contains the scores for the time series

def _uncythonized_model(self, beta):
        """ Creates the structure of the model

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

        Returns
        ----------
        theta : np.array
            Contains the predicted values for the time series

        Y : np.array
            Contains the length-adjusted time series (accounting for lags)

        scores : np.array
            Contains the scores for the time series
        """

        parm = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])
        theta = np.zeros(self.model_Y.shape[0])
        model_scale, model_shape, model_skewness = self._get_scale_and_shape(parm)

         # Loop over time series
        theta, self.model_scores = gas_llev_recursion(parm, theta, self.model_scores, self.model_Y, self.model_Y.shape[0], 
            self.family.score_function, self.link, model_scale, model_shape, model_skewness, self.max_lag)

        return theta, self.model_Y, self.model_scores