Toolbox: distfun

Title: Distfun

Summary: Distribution functions

Version: 1.1.3

Author: Michael Baudin
 Prateek Papriwal
 Pierre Lecuyer
 Luc Devroye
 Jean-Philippe Chancelier
 Michael A. Malcolm
 Cleve B. Moler
 George Marsaglia
 Arif Zaman
 Barry W. Brown

Maintainer: Administrator Atoms <atoms.admin@scilab.org>
 Prateek Papriwal <papriwalprateek@gmail.com>
 Michael Baudin <michael.baudin@contrib.scilab.org>

Category: Data Analysis - Statistics
 Data Analysis - Statistics - Statistics

Entity: Dassault Systèmes S.E.

WebSite:  https://gitlab.com/scilab/forge/distfun/

URL: http://atoms.scilab.org/toolboxes/distfun/1.1.3

License: CeCILL

ScilabVersion: >= 2025.1.0

Depends: ~ helptbx any
 ~ specfun any

Date: 2025-05-26

Description: The goal of this toolbox is to provide accurate distribution functions. 
 The provided functions are designed to be compatible with Matlab.
 
 The goals of this toolbox are the following.
  * All functions are tested with tables (actually, csv datasets).
    The tests includes accuracy tests, so that the accuracy 
    should by from 13 to 15 significant digits in most cases.
  * For each distribution, we have 
    * the probability distribution function (PDF)
    * the cumulated distribution function (CDF)
    * the inverse CDF
    * the random number generator
    * the statistics (mean and variance)
  * The CDF provides the upper and the lower tail of the 
    distribution, for accuracy reasons. 
  * The uniform random numbers are of high quality.
    The default is to use the Mersenne-Twister generator.   
  * Each function has a consistent help page.
    This removes confusions in the meaning 
    of the parameters and clarifies the differences 
    with other computing languages (e.g. R).
    
 The design is similar to Matlab's distribution functions. 
 A significant difference with Matlab's function is that both 
 the upper and lower tails are available in &quot;distfun&quot;, while 
 Matlab only provides the lower tail. 
 Hence, &quot;distfun&quot; should provide a better accuracy when 
 probabilities close to 1 are computed (e.g. p=0.9999). 
 
 There are many interesting, positive, differences with Scilab, Stixbox, or other
 tools. For a full set of motivations, please read :
 
 https://gitlab.com/scilab/forge/distfun/
    
 Features
 --------
 
 For each distribution x, we provide five functions :
  * distfun_xcdf : x CDF
  * distfun_xinv : x Inverse CDF
  * distfun_xpdf : x PDF
  * distfun_xrnd : x random numbers
  * distfun_xstat : x mean and variance
 
 Distributions available :
  * Beta (with x=beta)
  * Binomial (with x=bino)
  * Chi-Squared (with x=chi2)
  * Extreme Value (with x=ev)
  * Exponential (with x=exp)
  * F (with x=f)
  * Gamma (with x=gam)
  * Geometric (with x=geo)
  * Histogram (with x=histo)
  * Hypergeometric (with x=hyge)
  * Kolmogorov-Smirnov (with x=ks)
  * LogNormal (with x=logn)
  * LogUniform (with x=logu)
  * Multinomial (with x=mn)
  * Multivariate Normal (with x=mvn)
  * Negative Binomial (with x=nbin)
  * Noncentral F (with x=ncf)
  * Noncentral T (with x=nct)
  * Noncentral Chi-Squared (with x=ncx2)
  * Normal (with x=norm)
  * Poisson (with x=poi)
  * T (with x=t)
  * Truncated Normal (with x=tnorm)
  * Uniform Discrete (with x=unid)
  * Uniform (with x=unif)
  * Weibull (with x=wbl)
 
 Tutorial
  * dispfun_tutorial : A tutorial of the Distfun toolbox.
  * dispfun_plots : A collection of distribution function plots.
 
 Support
  * distfun_betainc : Regularized Incomplete Beta function
  * distfun_erfcinv : Inverse erfc function
  * distfun_gammainc : Regularized incomplete Gamma function
  * distfun_genericpdf : Compute the PDF from the CDF.
  * distfun_getpath : Returns path of current module
  * distfun_histocreate : Creates an histogram
  * distfun_inthisto : Discrete histogram
  * distfun_permrnd : Random permutation
  * distfun_plotintcdf :  Plots an integer CDF
  * distfun_verboseset : Set verbose mode.
 
 Weibull fitting
  * distfun_wblfit : Weibull parameter estimates
  * distfun_wblfitmm : Weibull parameter estimates with method of moments
  * distfun_wbllike : Weibull negative log-likelihood
  * distfun_wblplot : Weibull plot
 
 Other fitting functions
  * distfun_uniffitmm : Uniform parameter estimates with method of moments
  * distfun_betafitmm : Beta parameter estimates with method of moments
  * distfun_gamfitmm : Gamma parameter estimates with method of moments
 
 Random Number Generator
  * rng_overview : An overview of the Random Number Generators of the Distfun
 toolbox.
  * distfun_genget : Get the current random number generator
  * distfun_genset : Set the current random number generator
  * distfun_seedget : Get the current state of the current random number
 generator
  * distfun_seedset : Set the current state of the current random number
 generator
  * distfun_streamget : Get the current stream
  * distfun_streaminit : Initializes the current stream
  * distfun_streamset : Set the current stream
 
 Multivariate vectors
  * distfun_vectorrnd : Random vectors.
 