def deriv(self, p):
    "\n        Derivative of the logit transform\n\n        Parameters\n        ----------\n        p : array_like\n            Probabilities\n\n        Returns\n        -------\n        g'(p) : ndarray\n            Value of the derivative of logit transform at `p`\n\n        Notes\n        -----\n        g'(p) = 1 / (p * (1 - p))\n\n        Alias for `Logit`:\n        logit = Logit()\n        "
    p = self._clean(p)
    return (1.0 / (p * (1 - p)))
Derivative of the logit transform

Parameters
----------
p : array_like
    Probabilities

Returns
-------
g'(p) : ndarray
    Value of the derivative of logit transform at `p`

Notes
-----
g'(p) = 1 / (p * (1 - p))

Alias for `Logit`:
logit = Logit()
def deriv(self, p):
    "\n        Derivative of the logit transform\n\n        Parameters\n        ----------\n        p : array_like\n            Probabilities\n\n        Returns\n        -------\n        g'(p) : ndarray\n            Value of the derivative of logit transform at `p`\n\n        Notes\n        -----\n        g'(p) = 1 / (p * (1 - p))\n\n        Alias for `Logit`:\n        logit = Logit()\n        "
    p = self._clean(p)
    return (1.0 / (p * (1 - p)))