[R-pkgs] rTRNG: Advanced and Parallel Random Number Generation via TRNG

[Riccardo Porreca]

We are happy to announce the first CRAN release of rTRNG version 4.20-1.

rTRNG is a package for advanced parallel Random Number Generation in
R. It relies on TRNG (Tina’s Random Number Generator,
<https://numbercrunch.de/trng/>), a state-of-the-art C++ pseudo-random
number generator library for sequential and parallel Monte Carlo
simulations. In particular, parallel random number generators provided
by TRNG can be manipulated by jump and split operations. These allow
to jump ahead by an arbitrary number of steps and to split a sequence
into any desired sub-sequence(s), thus enabling techniques suitable to
parallel algorithms, such as block-splitting and leapfrogging.

The package provides the R user with access to the functionality of
the underlying TRNG C++ library. It embeds TRNG sources and headers
and makes them available to other projects combining R with C++.
Beyond this, rTRNG exposes the creation, manipulation and use of
pseudo-random streams to R, via Rcpp and RcppParallel.

rTRNG on CRAN
<https://cran.r-project.org/package=rTRNG>
Public repository
<https://github.com/miraisolutions/rTRNG#readme>

More on the history behind rTRNG and its way to CRAN at
<https://mirai-solutions.ch/news/2019/05/04/rTRNG-on-CRAN/>


-- 
Riccardo Porreca
Senior Solutions Consultant
Mirai Solutions GmbH
riccardo.porreca using mirai-solutions.com