A Tukey-Lambda continuous random variable.

Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:


x : array-like


q : array-like

lower or upper tail probability

lam : array-like

shape parameters

loc : array-like, optional

location parameter (default=0)

scale : array-like, optional

scale parameter (default=1)

size : int or tuple of ints, optional

shape of random variates (default computed from input arguments )

moments : string, optional

composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’)


tukeylambda.rvs(lam,loc=0,scale=1,size=1) :

  • random variates

tukeylambda.pdf(x,lam,loc=0,scale=1) :

  • probability density function

tukeylambda.cdf(x,lam,loc=0,scale=1) :

  • cumulative density function

tukeylambda.sf(x,lam,loc=0,scale=1) :

  • survival function (1-cdf — sometimes more accurate)

tukeylambda.ppf(q,lam,loc=0,scale=1) :

  • percent point function (inverse of cdf — percentiles)

tukeylambda.isf(q,lam,loc=0,scale=1) :

  • inverse survival function (inverse of sf)

tukeylambda.stats(lam,loc=0,scale=1,moments=’mv’) :

  • mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’)

tukeylambda.entropy(lam,loc=0,scale=1) :

  • (differential) entropy of the RV.,lam,loc=0,scale=1) :

  • Parameter estimates for tukeylambda data

Alternatively, the object may be called (as a function) to fix the shape, :

location, and scale parameters returning a “frozen” continuous RV object: :

rv = tukeylambda(lam,loc=0,scale=1) :

  • frozen RV object with the same methods but holding the given shape, location, and scale fixed


>>> import matplotlib.pyplot as plt
>>> numargs = tukeylambda.numargs
>>> [ lam ] = [0.9,]*numargs
>>> rv = tukeylambda(lam)

Display frozen pdf

>>> x = np.linspace(0,np.minimum(rv.dist.b,3))
>>> h=plt.plot(x,rv.pdf(x))

Check accuracy of cdf and ppf

>>> prb = tukeylambda.cdf(x,lam)
>>> h=plt.semilogy(np.abs(x-tukeylambda.ppf(prb,c))+1e-20)

Random number generation

>>> R = tukeylambda.rvs(lam,size=100)

Tukey-Lambda distribution

A flexible distribution ranging from Cauchy (lam=-1) to logistic (lam=0.0) to approx Normal (lam=0.14) to u-shape (lam = 0.5) to Uniform from -1 to 1 (lam = 1)

Previous topic


Next topic


This Page

Quick search