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:
install.packages('dqrng', repos = c(
rstub = 'https://rstub.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))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 0.143914583They 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) 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 <- 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 7.07ms 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 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] 0.0009574617It 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.