'Derivative of the power transform
Parameters
p : array-like
Mean parameters
Returns
g\'(p) : array
Derivative of power transform of `p`
Notes
g\'(`p`) = `power` * `p`**(`power` - 1)'

def deriv(self, p):

    if (self.power == 1):
        return np.ones_like(p)
    else:
        return (self.power * np.power(p, (self.power - 1)))
