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.
The currently released version is available from CRAN via
install.packages("dqrng")
Intermediate releases can also be obtained via r-universe:
options(repos = c(
rstub = 'https://rstub.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
install.packages('dqrng')
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] 9.266963 4.644899 9.607483 3.635770 4.742639
dqrexp(5, rate = 4)
#> [1] 0.111103883 0.084289794 0.003414377 0.042012033 0.143914583
They are quite a bit faster, though:
N <- 1e4
bm <- 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) 607µs 660.2µs 1451.
#> 2 dqrnorm(N) 89.8µs 92.7µs 9896.
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 × 4
#> expression min median `itr/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl>
#> 1 sample.int(m, n, replace = TRUE) 6.88ms 7.08ms 139.
#> 2 sample.int(1000 * m, n, replace = TRUE) 8.72ms 8.93ms 110.
#> 3 dqsample.int(m, n, replace = TRUE) 410.9µs 434.24µs 2137.
#> 4 dqsample.int(1000 * m, n, replace = TRUE) 397.74µs 435.38µs 1930.
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) 22.73ms 24.05ms 36.9
#> 2 sample.int(1000 * m, n) 12.07ms 13.85ms 68.1
#> 3 sample.int(m, n, useHash = TRUE) 9.57ms 12.63ms 74.4
#> 4 dqsample.int(m, n) 1.11ms 1.2ms 696.
#> 5 dqsample.int(1000 * m, n) 1.95ms 2.69ms 293.
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] 0.0009574617
It is also possible to register the supplied generators as user-supplied RNGs. This way set.seed()
and dqset.seed()
influence both (dq)runif
and (dq)rnorm
in the same way. This is also true for other r<dist>
functions, but note that rexp
and dqrexp
still give different results:
register_methods()
set.seed(4711); runif(5)
#> [1] 0.3143534 0.7835753 0.1443660 0.1109871 0.6433407
set.seed(4711); dqrunif(5)
#> [1] 0.3143534 0.7835753 0.1443660 0.1109871 0.6433407
dqset.seed(4711); rnorm(5)
#> [1] -0.3618122 0.8199887 -0.4075635 0.2073972 -0.8038326
dqset.seed(4711); dqrnorm(5)
#> [1] -0.3618122 0.8199887 -0.4075635 0.2073972 -0.8038326
set.seed(4711); rt(5, 10)
#> [1] -0.3196113 -0.4095873 -1.2928241 0.2399470 -0.1068945
dqset.seed(4711); rt(5, 10)
#> [1] -0.3196113 -0.4095873 -1.2928241 0.2399470 -0.1068945
set.seed(4711); rexp(5, 10)
#> [1] 0.0950560698 0.0567150561 0.1541222748 0.2512966671 0.0002175758
set.seed(4711); dqrexp(5, 10)
#> [1] 0.03254731 0.06855303 0.06977124 0.02579004 0.07629535
restore_methods()
All feedback (bug reports, security issues, feature requests, …) should be provided as issues.