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 :
http://forge.scilab.org/index.php/p/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)
* 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_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
