ncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.
    ncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.
    scipy.stats.ncf : Non-central F distribution.

    Notes
    -----
    This function calculates the CDF of the non-central f distribution using
    the Boost Math C++ library [1]_.

    The cumulative distribution function is computed using Formula 26.6.20 of
    [2]_:

    .. math::
        F(d_n, d_d, n_c, f) = \sum_{j=0}^\infty e^{-n_c/2}
        \frac{(n_c/2)^j}{j!} I_{x}(\frac{d_n}{2} + j, \frac{d_d}{2}),

    where :math:`I` is the regularized incomplete beta function, and
    :math:`x = f d_n/(f d_n + d_d)`.

    Note that argument order of `ncfdtr` is different from that of the
    similar ``cdf`` method of `scipy.stats.ncf`: `f` is the last
    parameter of `ncfdtr` but the first parameter of ``scipy.stats.ncf.cdf``.

    References
    ----------
    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
    .. [2] Milton Abramowitz and Irene A. Stegun, eds.
           Handbook of Mathematical Functions with Formulas,
           Graphs, and Mathematical Tables. New York: Dover, 1972.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy import special
    >>> from scipy import stats
    >>> import matplotlib.pyplot as plt

    Plot the CDF of the non-central F distribution, for nc=0.  Compare with the
    F-distribution from scipy.stats:

    >>> x = np.linspace(-1, 8, num=500)
    >>> dfn = 3
    >>> dfd = 2
    >>> ncf_stats = stats.f.cdf(x, dfn, dfd)
    >>> ncf_special = special.ncfdtr(dfn, dfd, 0, x)

    >>> fig = plt.figure()
    >>> ax = fig.add_subplot(111)
    >>> ax.plot(x, ncf_stats, 'b-', lw=3)
    >>> ax.plot(x, ncf_special, 'r-')
    >>> plt.show()

    Ú