def inverse_deriv2(self, z):
    "\n        Second derivative of the inverse link function g^(-1)(z).\n\n        Parameters\n        ----------\n        z : array_like\n            `z` is usually the linear predictor for a GLM or GEE model.\n\n        Returns\n        -------\n        g^(-1)''(z) : ndarray\n            The value of the second derivative of the inverse of the link\n            function\n\n        Notes\n        -----\n        This method should be overwritten by subclasses.\n\n        The inherited method is implemented through numerical differentiation.\n        "
    from statsmodels.tools.numdiff import _approx_fprime_scalar
    z = np.atleast_1d(z)
    return _approx_fprime_scalar(z, self.inverse_deriv, centered=True)
Second derivative of the inverse link function g^(-1)(z).

Parameters
----------
z : array_like
    `z` is usually the linear predictor for a GLM or GEE model.

Returns
-------
g^(-1)''(z) : ndarray
    The value of the second derivative of the inverse of the link
    function

Notes
-----
This method should be overwritten by subclasses.

The inherited method is implemented through numerical differentiation.
def inverse_deriv2(self, z):
    "\n        Second derivative of the inverse link function g^(-1)(z).\n\n        Parameters\n        ----------\n        z : array_like\n            `z` is usually the linear predictor for a GLM or GEE model.\n\n        Returns\n        -------\n        g^(-1)(z) : ndarray\n            The value of the second derivative of the inverse of the link\n            function\n\n        Notes\n        -----\n        This method should be overwritten by subclasses.\n\n        The inherited method is implemented through numerical differentiation.\n        "
    from statsmodels.tools.numdiff import _approx_fprime_scalar
    z = np.atleast_1d(z)
    return _approx_fprime_scalar(z, self.inverse_deriv, centered=True)