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If you need only a few truly random numbers you might use dice or atmospheric noise. However, if you need many random numbers you will have to use a pseudo random number generator (RNG). R includes support for different RNGs (c.f. ?Random) and a wide variety of distributions (c.f. ?distributions). The underlying methods have been well tested, but faster methods are available. The dqrng package provides fast random number generators with good statistical properties for usage with R. It combines these RNGs with fast distribution functions to sample from uniform, normal or exponential distributions.

## Installation

At the moment dqrng is not on CRAN, but you can install the current version via drat:

if (!requireNamespace("drat", quietly = TRUE)) install.packages("drat")
install.packages("dqrng")


## Usage

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

library(dqrng)
dqRNGkind("Xoroshiro128+")
dqset.seed(42)
dqrunif(5, min = 2, max = 10)
## [1] 8.480202 6.582408 8.869840 5.062206 8.828782
dqrnorm(5, mean = 3, sd = 5)
## [1] -4.0116902  0.9337035  5.6500975 -6.4582090  5.1763009
dqrexp(5, rate = 4)
## [1] 0.4428268 0.1011437 0.3526118 0.3793630 0.6327636


They are quite a bit faster, though, as we can see by comparing 10 million random draws from different distributions:

N <- 1e7
tm <- microbenchmark(
runif = runif(N),
dqrunif = dqrunif(N),
rnorm = rnorm(N),
dqrnorm = dqrnorm(N),
rexp = rexp(N),
dqrexp = dqrexp(N))

expr min lq mean median uq max neval cld
runif 248.16730 251.83371 262.20559 260.33073 265.69415 322.15771 100 d
dqrunif 34.77413 35.44569 39.40738 36.82459 38.42524 109.96758 100 a
rnorm 587.40975 596.92850 618.79356 613.08345 624.31043 706.79528 100 f
dqrnorm 63.17649 64.43796 68.77696 66.80184 68.39577 141.97466 100 c
rexp 392.79228 397.48715 413.66996 411.14180 420.42473 494.49631 100 e
dqrexp 52.75875 53.64510 57.15006 55.80021 58.65553 79.11577 100 b

For r* the default Mersenne-Twister was used, while dqr* used Xoroshiro128+ in this comparison. For rnorm the default inversion method was used, while dqrnorm (and dqrexp) used the Ziggurat algorithm from Boost.Random with additional tuning.

Both the RNGs and the distribution functions are distributed as C++ header-only library. See the included vignette for possible usage from C++.

## Supported Random Number Generators

Support for the following 64 bit RNGs is currently included:

• Mersenne-Twister
The 64 bit variant of the well-known Mersenne-Twister, which is also used as default. This is a conservative default that allows you to take advantage of the fast distribution functions provided by dqrng while staying close to R’s default RNG (32 bit Mersenne-Twister).
• pcg64
The default 64 bit variant from the PCG family developed by Melissa O’Neill. See http://www.pcg-random.org for more details.
• Xoroshiro128+, Xorshift128+, and Xorshift1024*
RNGs mainly developed by Sebastiano Vigna. They are used as default RNGs in Erlang and different JavaScript engines. See http://xoroshiro.di.unimi.it/ for more details.