.

Occasionally, it is needed to find the argument of the CDF for which
the resulting probability is very close to ``0`` or ``1`` - too close to
represent accurately with floating point arithmetic. In many cases,
however, the *logarithm* of this resulting probability may be
represented in floating point arithmetic, in which case this function
may be used to find the argument of the CDF for which the *logarithm*
of the resulting probability is :math:`y = \log(p)`.

The "logarithmic complement" of a number :math:`z` is mathematically
equivalent to :math:`\log(1-\exp(z))`, but it is computed to avoid loss
of precision when :math:`\exp(z)` is nearly :math:`0` or :math:`1`.

Examples
--------
Instantiate a distribution with the desired parameters:

>>> import numpy as np
>>> from scipy import stats
>>> X = stats.Uniform(a=-0.5, b=0.5)

Evaluate the inverse log-CDF at the desired argument:

>>> X.ilogcdf(-0.25)
0.2788007830714034
>>> np.allclose(X.ilogcdf(-0.25), X.icdf(np.exp(-0.25)))
True

r