It provides the following two main calls: [object Object],[object Object] It uses the Rbuilt-in uniform random number generator.
The unuran.new for a short introduction to the interface.
Additionally there are some shortcuts that allow provide an easy to
use interface: urdgt and urdau for
discrete distributions with given probability vectors,
and urtdr for log-concave distributions with given
density function.
To facilitate the use of the package for standard distributions the package also includes direct generation functions for approximately 30 standard distributions.
For continuous univariate distributions these calls are:
urbeta,
urburr,
urcauchy,
urchi,
urchisq,
urexp,
urextremeI,
urextremeII,
urf,
urgamma,
urgig,
urhyperbolic,
urlaplace,
urlnorm,
urlogis,
urlomax,
urnorm,
urpareto,
urplanck,
urpowerexp,
urrayleigh,
urt,
urtriang, and
urweibull.
For discrete distributions these are:
urbinom,
urgeom,
urhyper,
urlogarithmic,
urnbinom, and
urpois.
All these calls have similar syntax to the analogous Rbuilt-in
generating functions (if these exist) but have an optional domain
arguments lb and ub,
i.e., these calls also allow to draw samples from truncated
distributions:
ur...(n, distribution parameters, lb , ub)
Compared to the corresponding Rfunctions these ur functions
have a slightly different behavior.
ur functions are often much faster (e.g. a factor) of
about 5 for the $t$ distribution when the sample size is
large. For small samples the ur functions are slow.}
ur functions allow to sample from truncated
versions of the original distributions. Therefore the arguments
lb (lower border) and ub (upper border) are
available for all ur functions.}
ur functions are based on fast numerical
inversion algorithms. This is important for example for generating
order statistics or random vectors from copulas.}
ur functions do not allow vectors as
arguments (to be more precise: they only use the first element of
the vector).}G.~Tirler and J.~Leydold (2003): Automatic Nonuniform Random Variate Generation in R. In: K.~Hornik and F.~Leisch, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC~2003), March 20--22, Vienna, Austria.
unuran unuran.new,
unuran.sample, unuran.distr