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eddelbuettel

RcppZiggurat:'Rcpp' Integration of Different "Ziggurat" Normal RNG Implementations

The Ziggurat generator for normally distributed random numbers, originally proposed by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>) has been improved upon a few times starting with Leong et al (2005, <doi:10.18637/jss.v012.i07>). This package provides an aggregation in order to compare different implementations in order to provide a 'faster but good enough' alternative for use with R and C++ code. See the 'zigg' package for a lighter implementation for much easier use in other packages.

Maintained by Dirk Eddelbuettel. Last updated 1 days ago.

random-number-generatorsrcppzigguratgslcpp

12 stars 9.02 score 73 scripts 8 dependents

eddelbuettel

zigg:Lightweight Interfaces to the 'Ziggurat' Pseudo Random Number Generator

The 'Ziggurat' pseudo-random number generator (or PRNG), introduced by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>) and further improved by Leong et al (2005, <doi:10.18637/jss.v012.i07>), offers a lightweight and very fast PRNG for the normal, exponential, and uniform distributions. It is provided here in a small zero-dependency package. It can be used from R as well as from 'C/C++' code in other packages as is demonstrated by four included sample packages using four distinct methods to use the PRNG presented here in client package. The implementation is influenced by our package 'RcppZiggurat' which offers a comparison among multiple alternative implementations but presented here in a lighter-weight implementation that is easier to use by other packages. The PRNGs provided are generally faster than the ones in base R: on our machine, the relative gains for normal, exponential and uniform are on the order of 7.4, 5.2 and 4.7 times faster than base R. However, these generators are of potentially lesser quality and shorter period so if in doubt use of the base R functions remains the general recommendation.

Maintained by Dirk Eddelbuettel. Last updated 2 months ago.

cpp

4 stars 7.10 score 4 scripts 52 dependents

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