Category Archives: Packages

Useful packages for R.

dqsample: A bias-free alternative to base::sample()

For many tasks in statistics and data science it is useful to create a random sample or permutation of a data set. Within R the function base::sample() is used for this task. Unfortunately this function uses a slightly biased algorithm for creating random integers within a given range. Most recently this issue has been discussed in a thread on R-devel, which is also the motivation of the dqsample package. Currently dqsample is not on CRAN, but it can be installed via drat:

Example for the bias

When sampling many random integers the density of odd and even numbers should be roughly equal and constant. However, this is not the case with base::sample:

plot of chunk base

Or with slightly different parameters:

plot of chunk base-oszi

This particular example for the bias was found by Duncan Murdoch.

In dqsample the algorithm suggested by Daniel Lemire (2018, <arXiv:1805.1094>) is used. With this algorithm there is no observable bias between odd and even numbers:

plot of chunk dqsample

Where does the bias come from?

Internally the base::sample() function needs uniformly distributed random integers in an half-open range [0, n). In order to do so, R uses random floating point numbers that are uniformly distributed in [0, 1), multiplies by n and truncates the result to the next smaller integer. This method would be fine, if the random numbers used as starting point would be real numbers in the mathematical sense. However, this is not the case here.

The default random-number generator in R is a 32 bit version of the Mersenne-Twister. It produces random integers uniformly distributed in [0, 2^32), which are then divided by 2^32 to produce doubles in [0, 1). We can now invert the procedure described above to see how many integers are mapped to a certain result. For example, we could simulate rolling ten dice using sample(6, 10, replace = TRUE). Since 2^32 is not a multiple of six, the distribution cannot be completely even:

We see that both one and four are very slightly less likely than the other numbers. This effect gets much more pronounced as the number of items increases from which one can choose. For example, we can use the m from above to see how that uneven distribution of odd and even numbers came about:

Here we see that while only two integers map to any odd number, there are three integers mapped to the even numbers. This pattern shifts half way through the possible results, making the odd numbers more likely, leading to the first image displayed above. As one goes away from m, these pattern shifts occur more rapidly, leading to the oscillatory behaviour seen in the second image. As one moves further away from m, these oscillations happen so rapidly, that a density plot of odd and even numbers looks constant, but the bias is still there. For example, for m - 2^20 one such pattern shift happens between 982 and 983:

Below this point, even numbers are more likely than odd numbers. After this point, the pattern is reversed.


The algorithm used by base::sample() is biased with non-negligible effects when sampling from large data sets. The dqsample package provides an unbiased algorithm for the most common cases. It can be used as a drop-in replacement for the functionality provided by base::sample().

tikzDevice has a new home

Back in February the tikzDevice package became ORPHANED on CRAN. Consequently Kirill Müller and Yihui Xie searched for a new maintainer. When I read about it some time later, we decided that it makes sense for us to step in here. After a brief mail exchange with Yihui Xie the GitHub repository was transfered to our organization and can now be found at Meanwhile I have implemented fixes for the existing warning messages from the CRAN checks and uploaded version 0.12, which is currently making its’ way onto CRAN. The next steps will be to work through the existing issues.

What can one do with the tikzDevice? It is a graphics device similar to pdf() or png(). But instead of an image file that might be included in a report as external graphic, it generates files in the TikZ format that makes LaTeX generate the graphic. This enables consistent fonts between text and graphics and TeX’s capabilities for typesetting mathematical equations within graphics. The pdf vignette contains many examples.

One can even use it in a R-markdown document. A document using

in the YAML header and

in a setup chunk will use the same fonts for text and graphics when those are created with dev = "tikz". In this example Palatino with text-figures:

Example Palatino with text-figures

First CRAN release for dqrng

The dqrng package is now available from CRAN. It is possible to install it using

Besides this simplified installation the included RNGs have been updated: Xorshift128+ and Xorshift1024* have been removed in favor of the new Xorshiro256+, c.f. Using the provided RNGs from R is unchanged:

Fast Random Numbers for R with dqrng

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.


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


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

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

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

plot of chunk unnamed-chunk-4

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 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 for more details.

RcppArrayFire 0.0.2: Rcpp integration for ArrayFire

The RcppArrayFire package uses Rcpp to provide an interface from R to and from the ArrayFire library, an open source library that can make use of GPUs and other hardware accelerators via CUDA or OpenCL.

The official R bindings expose ArrayFire data structures as objects in R, which would require a large amount of code to support all the methods defined in ArrayFire’s C/C++ API. RcppArrayFire instead, which is derived from RcppFire by Kazuki Fukui, follows the lead of packages like RcppArmadillo or RcppEigen to provide seamless communication between R and ArrayFire at the C++ level.


Please note that currently RcppArrayFire has only been tested on Linux systems.


In order to use RcppArrayFire you will need development tools for R, Rcpp and the ArrayFire library and header files. On a sufficiently recent Debian based or derived system, this can be achieved with:

This will install the unified and CPU backends. The CUDA backend has not been packaged for Debian, and usage of the packaged OpenCL backend is hindered by a bug in the clBLAS package. For serious usage it is currently better to build from source or use the binary installer from ArrayFire:

In the last command you have to adjust the name of the installer script and (optionally) the installation prefix. For GPU support, you have to install additional drivers. For many build-in Intel GPUs, you can use

Installing CUDA or other OpenCL drivers is beyond the scope of this post, but see the ArrayFire documentation for details.

Package installation

RcppArrayFire is not on CRAN, but you can install the current version via drat:

If you have installed ArrayFire in a non-standard directory, you have to use the configure argument --with-arrayfire:


Calculating pi by simulation

Let’s look at the classical example of calculating pi via simulation. The basic idea is to generate a large number of random points within the unit square. An approximation for pi can then be calculated from the ratio of points within the unit circle to the total number of points. A vectorized implementation in R might look like this:

A simple way to use C++ code in R is to use the inline package or cppFunction() from Rcpp, which are both possible with RcppArrayFire. An implementation in C++ using ArrayFire might look like this:

Several things are worth noting:

(1) The syntax is almost identical. Besides the need for using types and a different function name when generating random numbers, the argument f32 to randu as well as the float type catches the eye. These instruct ArrayFire to use single precision floats, since not all devices support double precision floating point numbers. If you want to use double precision, you have to specify f64 and double.

(2) The results are not the same, since ArrayFire uses a different random number generator.

(3) The speed-up is quite impressive. However, sometimes the first invocation of a function is not as fast as expected due to the just-in-time compilation used by ArrayFire.

Arrays as parameters

Up to now we have only considered simple types like double or int as function parameters and return values. However, we can also use arrays. Consider the matrix product X’ X for a random matrix X in R:

The matrix multiplication can be implemented with RcppArrayFire using the appropriate matmul function:

Since an object of type af::array can contain different data types, the templated wrapper class RcppArrayFire::typed_array<> is used to indicate the desired data type when converting from R to C++. Again single precision floats are used with ArrayFire, which explains the difference between the two results. We can be sure that double precision is supported by switching the computation backend to “CPU”, which produces identical results:

Usage in a package

More serious functions should be defined in a permanent fashion. To facilitate this, RcppArrayFire contains the function RcppArraFire.package.skeleton(). This functions initialises a package with suitable configure script for linking with ArrayFire and RcppArrayFire. In order to implement new functionality you can then write C++ functions and save them in the src folder. Functions that should be callable from R should be marked with the [[Rcpp::export]] attribute. See the Rcpp vignettes on attributes and package writing for further details.


Besides testing the package on other platforms than Linux, future versions might include