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Fast Pseudo Random Number Generators for R

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daqana/dqrng

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R build statusCRAN statusdqrng status badgeCoverage statusDownloadsCII Best PracticesDependencies

dqrng

The dqrng package provides fast random number generators (RNG) with goodstatistical properties for usage with R. It combines these RNGs withfast distribution functions to sample from uniform, normal orexponential distributions. Both the RNGs and the distribution functionsare distributed as C++ header-only library.

Installation

The currently released version is available from CRAN via

install.packages("dqrng")

Intermediate releases can also be obtained viar-universe:

install.packages('dqrng',repos= c(rstub='https://rstub.r-universe.dev',CRAN='https://cloud.r-project.org'))

Example

Using the provided RNGs from R is deliberately similar to using R’sbuild-in RNGs:

library(dqrng)dqset.seed(42)dqrunif(5,min=2,max=10)#> [1] 9.266963 4.644899 9.607483 3.635770 4.742639dqrexp(5,rate=4)#> [1] 0.111103883 0.084289794 0.003414377 0.042012033 0.143914583

They are quite a bit faster, though:

N<-1e4bm<-bench::mark(rnorm(N), dqrnorm(N),check=FALSE)bm[,1:4]#> # A tibble: 2 × 4#>   expression      min   median `itr/sec`#>   <bch:expr> <bch:tm> <bch:tm>     <dbl>#> 1 rnorm(N)    606.2µs  654.7µs     1456.#> 2 dqrnorm(N)   82.8µs   85.9µs    10984.

This is also true for the provided sampling functions with replacement:

m<-1e7n<-1e5bm<-bench::mark(sample.int(m,n,replace=TRUE),                  sample.int(1e3*m,n,replace=TRUE),                  dqsample.int(m,n,replace=TRUE),                  dqsample.int(1e3*m,n,replace=TRUE),check=FALSE)bm[,1:4]#> # A tibble: 4 × 4#>   expression                                     min   median `itr/sec`#>   <bch:expr>                                <bch:tm> <bch:tm>     <dbl>#> 1 sample.int(m, n, replace = TRUE)            6.88ms   7.07ms      140.#> 2 sample.int(1000 * m, n, replace = TRUE)     8.57ms   8.81ms      112.#> 3 dqsample.int(m, n, replace = TRUE)        289.69µs 296.86µs     2834.#> 4 dqsample.int(1000 * m, n, replace = TRUE) 407.45µs 489.33µs     1645.

And without replacement:

bm<-bench::mark(sample.int(m,n),                  sample.int(1e3*m,n),                  sample.int(m,n,useHash=TRUE),                  dqsample.int(m,n),                  dqsample.int(1e3*m,n),check=FALSE)#> Warning: Some expressions had a GC in every iteration; so filtering is#> disabled.bm[,1:4]#> # A tibble: 5 × 4#>   expression                            min   median `itr/sec`#>   <bch:expr>                       <bch:tm> <bch:tm>     <dbl>#> 1 sample.int(m, n)                  40.98ms   42.8ms      23.1#> 2 sample.int(1000 * m, n)           12.01ms   13.3ms      66.9#> 3 sample.int(m, n, useHash = TRUE)   9.35ms   10.4ms      92.4#> 4 dqsample.int(m, n)               616.34µs  679.1µs    1265.#> 5 dqsample.int(1000 * m, n)          1.42ms    1.7ms     501.

Note that sampling from10^10 elements triggers “long-vector support”in R.

In addition the RNGs provide support for multiple independent streamsfor parallel usage:

N<-1e7dqset.seed(42,1)u1<- dqrunif(N)dqset.seed(42,2)u2<- dqrunif(N)cor(u1,u2)#> [1] 0.0009574617

It is also possible to register the supplied generators as user-suppliedRNGs. This wayset.seed() anddqset.seed() influence both(dq)runif and(dq)rnorm in the same way. This is also true for otherr<dist> functions, but note thatrexp anddqrexp still givedifferent results:

register_methods()set.seed(4711); runif(5)#> [1] 0.3143534 0.7835753 0.1443660 0.1109871 0.6433407set.seed(4711); dqrunif(5)#> [1] 0.3143534 0.7835753 0.1443660 0.1109871 0.6433407dqset.seed(4711); rnorm(5)#> [1] -0.3618122  0.8199887 -0.4075635  0.2073972 -0.8038326dqset.seed(4711); dqrnorm(5)#> [1] -0.3618122  0.8199887 -0.4075635  0.2073972 -0.8038326set.seed(4711); rt(5,10)#> [1] -0.3196113 -0.4095873 -1.2928241  0.2399470 -0.1068945dqset.seed(4711); rt(5,10)#> [1] -0.3196113 -0.4095873 -1.2928241  0.2399470 -0.1068945set.seed(4711); rexp(5,10)#> [1] 0.0950560698 0.0567150561 0.1541222748 0.2512966671 0.0002175758set.seed(4711); dqrexp(5,10)#> [1] 0.03254731 0.06855303 0.06977124 0.02579004 0.07629535restore_methods()

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