This section collects various additional functions and methods for statistical 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. |
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 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 | a class for non-linear monotonic transformation of a continuous 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) |