dqrng

The dqrng package provides fast random number generators (RNG) with good statistical properties for usage with R. It combines these RNGs with fast distribution functions to sample from uniform, normal or exponential distributions. Both the RNGs and the distribution functions are 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 via drat:

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

Example

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

library(dqrng)
dqset.seed(42)
dqrunif(5, min = 2, max = 10)
#> [1] 9.211802 2.616041 6.236331 4.588535 5.764814
dqrexp(5, rate = 4)
#> [1] 0.35118613 0.17656197 0.06844976 0.16984095 0.10096744

They are quite a bit faster, though:

N <- 1e4
bm <- bench::mark(rnorm(N), dqrnorm(N), check = FALSE)
bm[, 1:4]
#> # A tibble: 2 x 4
#>   expression      min   median `itr/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl>
#> 1 rnorm(N)    598.9µs    670µs     1414.
#> 2 dqrnorm(N)   85.5µs     89µs     9663.

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

m <- 1e7
n <- 1e5
bm <- 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 x 4
#>   expression                                     min   median `itr/sec`
#>   <bch:expr>                                <bch:tm> <bch:tm>     <dbl>
#> 1 sample.int(m, n, replace = TRUE)            6.94ms   7.52ms      131.
#> 2 sample.int(1000 * m, n, replace = TRUE)      8.8ms   9.64ms      101.
#> 3 dqsample.int(m, n, replace = TRUE)        304.75µs 444.96µs     2207.
#> 4 dqsample.int(1000 * m, n, replace = TRUE) 397.96µs 675.24µs     1502.

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 x 4
#>   expression                            min   median `itr/sec`
#>   <bch:expr>                       <bch:tm> <bch:tm>     <dbl>
#> 1 sample.int(m, n)                  38.59ms  51.73ms      19.6
#> 2 sample.int(1000 * m, n)           11.98ms  15.34ms      63.9
#> 3 sample.int(m, n, useHash = TRUE)   9.94ms  12.73ms      71.5
#> 4 dqsample.int(m, n)               942.04µs   1.05ms     755. 
#> 5 dqsample.int(1000 * m, n)          1.86ms   2.44ms     315.

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

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

N <- 1e7
dqset.seed(42, 1)
u1 <- dqrunif(N)
dqset.seed(42, 2)
u2 <- dqrunif(N)
cor(u1, u2)
#> [1] -0.0005787967

Feedback

All feedback (bug reports, security issues, feature requests, …) should be provided as issues.