Distributions

This section collects various additional functions and methods for statistical distributions.

Empirical Distributions

ECDF(x[, side])	Return the Empirical CDF of an array as a step function.
StepFunction(x, y[, ival, sorted, side])	A basic step function.
monotone_fn_inverter(fn, x[, vectorized])	Given a monotone function fn (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x.

Distribution Extras

Skew Distributions

SkewNorm_gen()	univariate Skew-Normal distribution of Azzalini
SkewNorm2_gen([momtype, a, b, xtol, …])	univariate Skew-Normal distribution of Azzalini
ACSkewT_gen()	univariate Skew-T distribution of Azzalini
skewnorm2	univariate Skew-Normal distribution of Azzalini

Distributions based on Gram-Charlier expansion

pdf_moments_st(cnt)	Return the Gaussian expanded pdf function given the list of central moments (first one is mean).
pdf_mvsk(mvsk)	Return the Gaussian expanded pdf function given the list of 1st, 2nd moment and skew and Fisher (excess) kurtosis.
pdf_moments(cnt)	Return the Gaussian expanded pdf function given the list of central moments (first one is mean).
NormExpan_gen(args, **kwds)	Gram-Charlier Expansion of Normal distribution

cdf of multivariate normal wrapper for scipy.stats

mvstdnormcdf(lower, upper, corrcoef, **kwds)	standardized multivariate normal cumulative distribution function
mvnormcdf(upper, mu, cov[, lower])	multivariate normal cumulative distribution function

Univariate Distributions by non-linear Transformations

Univariate distributions can be generated from a non-linear transformation of an existing univariate distribution. Transf_gen is a class that can generate a new distribution from a monotonic transformation, TransfTwo_gen can use hump-shaped or u-shaped transformation, such as abs or square. The remaining objects are special cases.

TransfTwo_gen(kls, func, funcinvplus, …)	Distribution based on a non-monotonic (u- or hump-shaped transformation)
Transf_gen(kls, func, funcinv, *args, **kwargs)	a class for non-linear monotonic transformation of a continuous random variable
ExpTransf_gen(kls, *args, **kwargs)	Distribution based on log/exp transformation
LogTransf_gen(kls, *args, **kwargs)	Distribution based on log/exp transformation
SquareFunc	class to hold quadratic function with inverse function and derivative
absnormalg	Distribution based on a non-monotonic (u- or hump-shaped transformation)
invdnormalg	a class for non-linear monotonic transformation of a continuous random variable
loggammaexpg	univariate distribution of a non-linear monotonic transformation of a random variable
lognormalg	a class for non-linear monotonic transformation of a continuous random variable
negsquarenormalg	Distribution based on a non-monotonic (u- or hump-shaped transformation)
squarenormalg	Distribution based on a non-monotonic (u- or hump-shaped transformation)
squaretg	Distribution based on a non-monotonic (u- or hump-shaped transformation)