def inverse_deriv(self, z):
    "\n        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 derivative of the inverse of the link function\n\n        Notes\n        -----\n        This reference implementation gives the correct result but is\n        inefficient, so it can be overridden in subclasses.\n        "
    return (1 / self.deriv(self.inverse(z)))
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 derivative of the inverse of the link function

Notes
-----
This reference implementation gives the correct result but is
inefficient, so it can be overridden in subclasses.
def inverse_deriv(self, z):
    "\n        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 derivative of the inverse of the link function\n\n        Notes\n        -----\n        This reference implementation gives the correct result but is\n        inefficient, so it can be overridden in subclasses.\n        "
    return (1 / self.deriv(self.inverse(z)))