def convenience_split_params(self, params, return_all_types=False):
        """
        Splits parameter vector into nest parameters and index parameters.

        Parameters
        ----------
        all_params : 1D ndarray.
            Should contain all of the parameters being estimated (i.e. all the
            nest coefficients and all of the index coefficients). All elements
            should be ints, floats, or longs.
        rows_to_nests : 2D scipy sparse array.
            There should be one row per observation per available alternative
            and one column per nest. This matrix maps the rows of the design
            matrix to the unique nests (on the columns).
        return_all_types : bool, optional.
            Determines whether or not a tuple of 4 elements will be returned
            (with one element for the nest, shape, intercept, and index
            parameters for this model). If False, a tuple of 2 elements will
            be returned, as described below. The tuple will contain the nest
            parameters and the index coefficients.

        Returns
        -------
        orig_nest_coefs : 1D ndarray.
            The nest coefficients being used for estimation. Note that these
            values are the logit of the inverse of the scale parameters for
            each lower level nest.
        index_coefs : 1D ndarray.
            The index coefficients of this nested logit model.

        Note
        ----
        If `return_all_types == True` then the function will return a tuple of
        four objects. In order, these objects will either be None or the arrays
        representing the arrays corresponding to the nest, shape, intercept,
        and index parameters.
        """
        return split_param_vec(params,
                               self.rows_to_nests,
                               return_all_types=return_all_types)
Splits parameter vector into nest parameters and index parameters.

        Parameters
        ----------
        all_params : 1D ndarray.
            Should contain all of the parameters being estimated (i.e. all the
            nest coefficients and all of the index coefficients). All elements
            should be ints, floats, or longs.
        rows_to_nests : 2D scipy sparse array.
            There should be one row per observation per available alternative
            and one column per nest. This matrix maps the rows of the design
            matrix to the unique nests (on the columns).
        return_all_types : bool, optional.
            Determines whether or not a tuple of 4 elements will be returned
            (with one element for the nest, shape, intercept, and index
            parameters for this model). If False, a tuple of 2 elements will
            be returned, as described below. The tuple will contain the nest
            parameters and the index coefficients.

        Returns
        -------
        orig_nest_coefs : 1D ndarray.
            The nest coefficients being used for estimation. Note that these
            values are the logit of the inverse of the scale parameters for
            each lower level nest.
        index_coefs : 1D ndarray.
            The index coefficients of this nested logit model.

        Note
        ----
        If `return_all_types == True` then the function will return a tuple of
        four objects. In order, these objects will either be None or the arrays
        representing the arrays corresponding to the nest, shape, intercept,
        and index parameters.