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Distribution functions
(15 downloads for this version - 18451 downloads for all versions)
Michael Baudin
Prateek Papriwal
Pierre Lecuyer
Luc Devroye
Jean-Philippe Chancelier
Michael A. Malcolm
Cleve B. Moler
George Marsaglia
Arif Zaman
Barry W. Brown
James Lovato
Kathy Russell
Makoto Matsumoto
Takuji Nishimura
Bruno Pincon
Richard Brent
John Burkardt
INRIA, DIGITEO and others
Package maintainers
Prateek Papriwal
Michael Baudin
Supported Scilab Versions
>= 5.5.0
Helptbx (any version)
Apifun (≥ 0.4)
Specfun (≥ 0.1)
Creation Date
October 10, 2015
ATOMS packaging system
This module is being packaged
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 "distfun", while Matlab only provides the lower tail. Hence, "distfun" 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 : 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.
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Comment from Michael Baudin -- October 10, 2015, 08:55:05 PM    
This is the changelog.

distfun (1.0)
 * Fixed ticket #1526
   Fixed function name for Multivariate Normal.
To update your code, please replace :


with :


 * Created distfun_vectorrnd : vector random number generator 
   with independent or gaussian copula. 
 * Created new histogram distribution. 
   This is useful when lots of datas are available.
   This fixes ticket #1514.
 * Fixed ticket #1585
   distfun_wblplot failed with Scilab 5.5.2.
   Used backslash instead of regress.

distfun (v0.9.1)
 * Updated distfun_genericpdf to use numderivative, 
   for Scilab 5.5.0
   Idem for distfun_wblfit.
 * Updated distfun_wblstat to update vector^scalar 
   syntax for Scilab 5.5.0
   Idem for distfun_unidstat.
 * Added Rejection algorithm demo : Normal distribution, 
   based on instrumental Cauchy distribution.

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