def deriv(self, p):
    "\n        Derivative of the power transform\n\n        Parameters\n        ----------\n        p : array_like\n            Mean parameters\n\n        Returns\n        -------\n        g'(p) : ndarray\n            Derivative of power transform of `p`\n\n        Notes\n        -----\n        g'(`p`) = `power` * `p`**(`power` - 1)\n        "
    if (self.power == 1):
        return np.ones_like(p)
    else:
        return (self.power * np.power(p, (self.power - 1)))
Derivative of the power transform

Parameters
----------
p : array_like
    Mean parameters

Returns
-------
g'(p) : ndarray
    Derivative of power transform of `p`

Notes
-----
g'(`p`) = `power` * `p`**(`power` - 1)
def deriv(self, p):
    "\n        Derivative of the power transform\n\n        Parameters\n        ----------\n        p : array_like\n            Mean parameters\n\n        Returns\n        -------\n        g'(p) : ndarray\n            Derivative of power transform of `p`\n\n        Notes\n        -----\n        g'(`p`) = `power` * `p`**(`power` - 1)\n        "
    if (self.power == 1):
        return np.ones_like(p)
    else:
        return (self.power * np.power(p, (self.power - 1)))