'The logit transform
Parameters
p : array-like
Probabilities
Returns
z : array
Logit transform of `p`
Notes
g(p) = log(p / (1 - p))'

def __call__(self, p):

    p = self._clean(p)
    return np.log((p / (1.0 - p)))
