Blog Archives

Fast sampling support in dqrng

April 8, 2019
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Fast sampling support in dqrng

About half a year ago I wrote about dqsample providing a bias free alternative to base::sample(). Meanwhile this is mostly of historic interest, since the upcomming R release 3.6.0 will see an updated sampling algorithm. However, some of the techniques used in dqsample are now part of dqrng. Of course, the sampling method is still

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dqrng v0.1.0: breaking changes

March 5, 2019
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A new version of dqrng has made it onto the CRAN servers. This version brings two breaking changes, hence the “larger than usual” change in version number: An integer vector instead of a single int is used for seeding (Aaron Lun in #10) Single integer seeds lead to a different RNG state than before. dqrng::dqset_seed()

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dqrng v0.0.5: New and updated RNGs

February 1, 2019
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A new version of dqrng has made it onto the CRAN servers after a brief hick-up. Thanks to the CRAN team in general and Uwe Ligges in particular for their relentless efforts. This versions adds a new RNG to be used together with the provided distribution functions: The 64 bit version of the 20 rounds

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

October 9, 2018
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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

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tikzDevice has a new home

July 2, 2018
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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

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First CRAN release for dqrng

May 14, 2018
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The dqrng package is now available from CRAN. It is possible to install it using install.packages("dqrng") Besides this simplified installation the included RNGs have been updated: Xorshift128+ and Xorshift1024* have been removed in favor of the new Xorshiro256+, c.f. http://xoshiro.di.unimi.it/. Using the provided RNGs from R is unchanged: library(dqrng) dqRNGkind("Xoroshiro128+") dqset.seed(42) dqrunif(5, min = 2,

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Fast Random Numbers for R with dqrng

April 20, 2018
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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

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