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. |

`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. |

*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` | univariate distribution of a non-linear monotonic transformation of a |

`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) |

© 2009–2012 Statsmodels Developers

© 2006–2008 Scipy Developers

© 2006 Jonathan E. Taylor

Licensed under the 3-clause BSD License.

http://www.statsmodels.org/stable/distributions.html