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<!-- README.md is generated from README.Rmd. Please edit that file -->
[](https://travis-ci.org/daqana/dqrng)
[](https://ci.appveyor.com/project/rstub/dqrng)
[](https://cran.r-project.org/package=dqrng)
[](https://codecov.io/github/daqana/dqrng?branch=master)
[](https://www.r-pkg.org/pkg/dqrng)
[](https://bestpractices.coreinfrastructure.org/projects/2157)
[](https://www.codacy.com/app/rstub/dqrng?utm_source=github.com&utm_medium=referral&utm_content=daqana/dqrng&utm_campaign=Badge_Grade)
[](https://cran.r-project.org/package=dqrng)
# 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
``` r
install.packages("dqrng")
```
Intermediate releases can also be obtained via
[drat](https://cran.r-project.org/package=drat):
``` r
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:
``` r
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:
``` r
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) 630µs 720.7µs 1366.
#> 2 dqrnorm(N) 71.3µs 80.9µs 11897.
```
This is also true for the provided sampling functions with replacement:
``` r
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) 5.93ms 6.39ms 153.
#> 2 sample.int(1000 * m, n, replace = TRUE) 7.26ms 7.85ms 127.
#> 3 dqsample.int(m, n, replace = TRUE) 288.92µs 339.85µs 2774.
#> 4 dqsample.int(1000 * m, n, replace = TRUE) 346.69µs 379.22µs 2369.
```
And without replacement:
``` r
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) 34.22ms 36.02ms 26.0
#> 2 sample.int(1000 * m, n) 11.99ms 12.97ms 72.6
#> 3 sample.int(m, n, useHash = TRUE) 9.3ms 10.09ms 92.7
#> 4 dqsample.int(m, n) 1.34ms 1.49ms 596.
#> 5 dqsample.int(1000 * m, n) 1.69ms 2.03ms 434.
```
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:
``` r
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](https://github.com/daqana/dqrng/issues).
|
