Advanced and Parallel Random Number Generation via 'TRNG'
c(person("Riccardo", "Porreca", role = c("aut", "cre"),
email = "riccardo.porreca@mirai-solutions.com"),
person("Roland", "Schmid", role = "aut",
email = "roland.schmid@mirai-solutions.com"),
person("Mirai Solutions GmbH", role = "cph",
email = "info@mirai-solutions.com"),
person("Heiko", "Bauke", role = c("ctb", "cph"),
comment = "TRNG sources and headers"))
Embeds sources and headers from Tina's Random
Number Generator ('TRNG') C++ library. Exposes some functionality for
easier access, testing and benchmarking into R. Provides examples of
how to use parallel RNG with 'RcppParallel'. The methods and
techniques behind 'TRNG' are illustrated in the package vignettes and
examples. Full documentation is available in Bauke (2018)
<https://numbercrunch.de/trng/trng.pdf>.
covr, knitr, R.rsp, rmarkdown, testthat (>= 2.0.0)
'LdFlags.R' 'RcppExports.R' 'TRNG.Engine.R' 'TRNG.Random.R'
'TRNGkind.R' 'TRNGseed.R' 'TRNGjump.R' 'TRNGsplit.R'
'TRNG.Random.seed.R' 'TRNG.Version.R' 'currentEngine.R'
'defaultKind.R' 'inline.R' 'rTRNG-package.R' 'rbinom_trng.R'
'rlnorm_trng.R' 'rnorm_trng.R' 'rpois_trng.R' 'runif_trng.R'
'zzz.R'
Riccardo Porreca [aut, cre],
Roland Schmid [aut],
Mirai Solutions GmbH [cph],
Heiko Bauke [ctb, cph] (TRNG sources and headers)

Embeds sources and headers from Tina's Random
Number Generator ('TRNG') C++ library. Exposes some functionality for
easier access, testing and benchmarking into R. Provides examples of
how to use parallel RNG with 'RcppParallel'. The methods and
techniques behind 'TRNG' are illustrated in the package vignettes and
examples. Full documentation is available in Bauke (2021)
<https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.

Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function.