Distribution Functions toolbox
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.
* 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 :
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-Square (with x=chi2)
* Exponential (with x=exp)
* F (with x=f)
* Gamma (with x=gam)
* Geometric (with x=geo)
* Hypergeometric (with x=hyge)
* LogNormal (with x=logn)
* Normal (with x=norm)
* Poisson (with x=poi)
* T (with x=t)
* Uniform (with x=unif)
* dispfun_tutorial — A tutorial of the Distfun toolbox.
* dispfun_plots — A collection of distribution function plots.
* distfun_cov — Returns the empirical covariance matrix
* distfun_erfcinv — Inverse erfc function
* distfun_genericpdf — Compute the PDF from the CDF.
* distfun_getpath — Returns path of current module
* distfun_inthisto — Discrete histogram
* distfun_plotintcdf - Plots an integer CDF
Random Number Generator
* rng_overview — An overview of the Random Number Generators of the Distfun
* 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
* distfun_seedset — Set the current state of the current random number
* distfun_streamget — Get the current stream
* distfun_streaminit — Initializes the current stream
* distfun_streamset — Set the current stream