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# scipy.special.tklmbda

scipy.special.tklmbda(x, lmbda, out=None) = <ufunc 'tklmbda'>

  Cumulative distribution function of the Tukey lambda distribution.

  Parameters:
    x, lmbdaarray_like

      Parameters

    outndarray, optional

      Optional output array for the function results

  Returns:
    cdfscalar or ndarray

      Value of the Tukey lambda CDF

  See also

  `scipy.stats.tukeylambda`

    Tukey lambda distribution

  Examples

      >>> import numpy as np
      >>> import matplotlib.pyplot as plt
      >>> from scipy.special import tklmbda, expit

  Compute the cumulative distribution function (CDF) of the Tukey lambda distribution at several `x` values for `lmbda` = -1.5.

      >>> x = np.linspace(-2, 2, 9)
      >>> x
      array([-2. , -1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ])
      >>> tklmbda(x, -1.5)
      array([0.34688734, 0.3786554 , 0.41528805, 0.45629737, 0.5       ,
             0.54370263, 0.58471195, 0.6213446 , 0.65311266])

  When `lmbda` is 0, the function is the logistic sigmoid function, which is implemented in `scipy.special` as `expit`.

      >>> tklmbda(x, 0)
      array([0.11920292, 0.18242552, 0.26894142, 0.37754067, 0.5       ,
             0.62245933, 0.73105858, 0.81757448, 0.88079708])
      >>> expit(x)
      array([0.11920292, 0.18242552, 0.26894142, 0.37754067, 0.5       ,
             0.62245933, 0.73105858, 0.81757448, 0.88079708])

  When `lmbda` is 1, the Tukey lambda distribution is uniform on the interval [-1, 1], so the CDF increases linearly.

      >>> t = np.linspace(-1, 1, 9)
      >>> tklmbda(t, 1)
      array([0.   , 0.125, 0.25 , 0.375, 0.5  , 0.625, 0.75 , 0.875, 1.   ])

  In the following, we generate plots for several values of `lmbda`.

  The first figure shows graphs for `lmbda` <= 0.

      >>> styles = ['-', '-.', '--', ':']
      >>> fig, ax = plt.subplots()
      >>> x = np.linspace(-12, 12, 500)
      >>> for k, lmbda in enumerate([-1.0, -0.5, 0.0]):
      ...     y = tklmbda(x, lmbda)
      ...     ax.plot(x, y, styles[k], label=rf'$\lambda$ = {lmbda:-4.1f}')

      >>> ax.set_title(r'tklmbda(x, $\lambda$)')
      >>> ax.set_label('x')
      >>> ax.legend(framealpha=1, shadow=True)
      >>> ax.grid(True)

  The second figure shows graphs for `lmbda` > 0. The dots in the graphs show the bounds of the support of the distribution.

      >>> fig, ax = plt.subplots()
      >>> x = np.linspace(-4.2, 4.2, 500)
      >>> lmbdas = [0.25, 0.5, 1.0, 1.5]
      >>> for k, lmbda in enumerate(lmbdas):
      ...     y = tklmbda(x, lmbda)
      ...     ax.plot(x, y, styles[k], label=fr'$\lambda$ = {lmbda}')

      >>> ax.set_prop_cycle(None)
      >>> for lmbda in lmbdas:
      ...     ax.plot([-1/lmbda, 1/lmbda], [0, 1], '.', ms=8)

      >>> ax.set_title(r'tklmbda(x, $\lambda$)')
      >>> 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`.