.
    If the selected `method` is not available, a ``NotImplementedError``
    will be raised.

Returns
-------
out : array
    The log-CDF evaluated at the provided argument(s).

See Also
--------
cdf
logccdf

Notes
-----
Suppose a continuous probability distribution has support :math:`[l, r]`.
The log-CDF evaluates to its minimum value of :math:`\log(0) = -\infty`
for :math:`x ≤ l` and its maximum value of :math:`\log(1) = 0` for
:math:`x ≥ r`.

For distributions with infinite support, it is common for
`cdf` to return a value of ``0`` when the argument
is theoretically within the support; this can occur because the true value
of the CDF is too small to be represented by the chosen dtype. `logcdf`,
however, will often return a finite (not ``-inf``) result over a much larger
domain. Similarly, `logcdf` may provided a strictly negative result with
arguments for which `cdf` would return ``1.0``. Consequently, it may be
preferred to work with the logarithms of probabilities to avoid underflow
and related limitations of floating point numbers.

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`.
Similarly, the term "logarithmic difference" of :math:`w` and :math:`z`
is used here to mean :math:`\log(\exp(w)-\exp(z))`.

If ``y < x``, the CDF is negative, and therefore the log-CCDF
is complex with imaginary part :math:`\pi`. For
consistency, the result of this function always has complex dtype
when `y` is provided, regardless of the value of the imaginary part.

References
----------
.. [1] Cumulative distribution function, *Wikipedia*,
       https://en.wikipedia.org/wiki/Cumulative_distribution_function

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 log-CDF at the desired argument:

>>> X.logcdf(0.25)
-0.287682072451781
>>> np.allclose(X.logcdf(0.), np.log(X.cdf(0.)))
True

r