UNU.RAN - Universal Non-Uniform RANdom number generators |

UNU.RAN |

UNU.RAN (Universal Non-Uniform RAndom Number generator) is a collection of algorithms for generating non-uniform pseudorandom variates as a library of C functions designed and implemented by the ARVAG (Automatic Random VAriate Generation) project group in Vienna, and released under the GNU Public License (GPL). It is especially designed for such situations where

- a non-standard distribution or a truncated distribution is needed.
- experiments with different types of distributions are made.
- random variates for variance reduction techniques are used.
- fast generators of predictable quality are necessary.
Of course it is also well suited for standard distributions. However due to its more sophisticated programming interface it might not be as easy to use if you only look for a generator for the standard normal distribution. (Although UNU.RAN provides generators that are superior in many aspects to those found in quite a number of other libraries.)

UNU.RAN implements several methods for generating random numbers. The choice depends primary on the information about the distribution can be provided and - if the user is familar with the different methods - on the preferences of the user.

The design goals of UNU.RAN are to provide

reliable,portableandrobust(as far as this is possible) functions with aconsisentandeasy to useinterface. It is suitable for all situation where experiments with different distributions including non-standard distributions. For example it is no problem to replace the normal distribution by an empirical distribution in a model.Originally designed as a library for so called

black-boxoruniversal algorithmsits interface is different from other libraries. (Nevertheless it also contains special generators for standard distributions.) It does not provide subroutines for random variate generation for particular distributions. Instead it uses anobject-oriented interface.Distributionsandgeneratorsare treated as independent objects. This approach allows one not only to have different methods for generating non-uniform random variates. Thus it is possible to choose the one which is optimal in for the situation (e.g. speed, quality of random numbers, using for variance reduction techniques, etc.). It also allows to sample from non-standard distribution or even from distributions that arise in a model and can only be computed in a complicated subroutine.Sampling from a particular distribution requires the following steps:

- Create a distribution object. (Objects for standard distributions are available in the library)
- Choose a method.
- Initialize the generator, i.e., create the generator object. If the choosen method is not suitable for the given distribution (or if the distribution object contains too little information about the distribution) the initialization routine fails and produces an error message. Thus the generator object does (probably) not produce false results (random variates of a different distribution).
- Use this generator object to sample from the distribution.
For details see the online documentation.

There are four types of objects that can be manipulated independently:

Distribution objects: hold all information about the random variates that should be generated. The following types of distributions are available:

- Continuous and Discrete distributions
- Empirical distributions
- Multivariate distributions
Of course a library of standard distributions is included (and these can be further modified to get, e.g., truncated distributions). Moreover the library provides subroutines to build almost arbitrary distributions.

Generator objects: hold the generator for the given distribution. It is possible to build independent generator objects for the same distribution object which might use the same or different methods for generation. (If the choosen method is not suitable for the given method, a NULL pointer is returned in the initialization step).Parameter objects: Each transformation method requires several parameters to adjust the generator to a given distribution. The parameter object holds all this information. When created it contains all necessary default settings. It is only used to create a generator object and destroyed immediately.

Altough there is no need to change these parameters or even know about their existence for "usual distributions", they allow a fine tuning of the generator to work with distributions with some awkward properties. The library provides all necessary functions to change these default parameters.Uniform Random Number Generators: All generator objects need one (or more) streams of uniform random numbers that are transformed into random variates of the given distribution. These are given as pointers to appropriate functions or structures (objects). Two generator objects may have their own uniform random number generators or share a common one. Any functions that produce uniform (pseudo-) random numbers can be used. We suggest Otmar Lendl's PRNG library.The UNU.RAN Library is included in several software packages:

- ROOT (an Object-Oriented Data-Analysis Framework)
- The R Project for Statistical Computing (see package Runuran).
- SSJ (Stochastic Simulation in Java).
- EcoLab (software package and a research project that is looking at the dynamics of evolution).
For remarks, problems, questions, suggestions please contact Josef Leydold.

The current version of this package can be found at the home page of the ARVAG (Automatic Random VAriate Generation) project group in Vienna.

This article is translated to Serbo-Croatian language by Jovana Milutinovich from Web Geeks Resources.

UNU.RAN |

Josef Leydold (November 17, 2009) | Research supported by |