ambda$)')
    >>> ax.set_xlabel('x')
    >>> ax.legend(framealpha=1, shadow=True)
    >>> ax.grid(True)

    >>> plt.tight_layout()
    >>> plt.show()

    The CDF of the Tukey lambda distribution is also implemented as the
    ``cdf`` method of `scipy.stats.tukeylambda`.  In the following,
    ``tukeylambda.cdf(x, -0.5)`` and ``tklmbda(x, -0.5)`` compute the
    same values:

    >>> from scipy.stats import tukeylambda
    >>> x = np.linspace(-2, 2, 9)

    >>> tukeylambda.cdf(x, -0.5)
    array([0.21995157, 0.27093858, 0.33541677, 0.41328161, 0.5       ,
           0.58671839, 0.66458323, 0.72906142, 0.78004843])

    >>> tklmbda(x, -0.5)
    array([0.21995157, 0.27093858, 0.33541677, 0.41328161, 0.5       ,
           0.58671839, 0.66458323, 0.72906142, 0.78004843])

    The implementation in ``tukeylambda`` also provides location and scale
    parameters, and other methods such as ``pdf()`` (the probability
    density function) and ``ppf()`` (the inverse of the CDF), so for
    working with the Tukey lambda distribution, ``tukeylambda`` is more
    generally useful.  The primary advantage of ``tklmbda`` is that it is
    significantly faster than ``tukeylambda.cdf``.
    Ú