def inverse(self, z):
    '\n        Inverse of the logit transform\n\n        Parameters\n        ----------\n        z : array_like\n            The value of the logit transform at `p`\n\n        Returns\n        -------\n        p : ndarray\n            Probabilities\n\n        Notes\n        -----\n        g^(-1)(z) = exp(z)/(1+exp(z))\n        '
    z = np.asarray(z)
    t = np.exp((- z))
    return (1.0 / (1.0 + t))
Inverse of the logit transform

Parameters
----------
z : array_like
    The value of the logit transform at `p`

Returns
-------
p : ndarray
    Probabilities

Notes
-----
g^(-1)(z) = exp(z)/(1+exp(z))
def inverse(self, z):
    '\n        Inverse of the logit transform\n\n        Parameters\n        ----------\n        z : array_like\n            The value of the logit transform at `p`\n\n        Returns\n        -------\n        p : ndarray\n            Probabilities\n\n        Notes\n        -----\n        g^(-1)(z) = exp(z)/(1+exp(z))\n        '
    z = np.asarray(z)
    t = np.exp((- z))
    return (1.0 / (1.0 + t))