Henrik Bengtsson takes us through an interesting issue:

R does a superb job of taking care of us when it comes to random number generation – as long as we run our analysis sequentially in a single R process. Formally R uses the Mersenne Twister RNG algorithm [1] by default, which can we can set explicitly using

`RNGkind("Mersenne-Twister")`

. However, like many other RNG algorithms, the authors designed this one for generating random number sequentially but not in parallel. If we use it in parallel code, there is a risk that there will a correlation between the random numbers generated in parallel, and, when taken together, they may no longer be “random enough” for our needs.

The post covers how the `future`

package has your back when it comes to random numbers. H/T R-Bloggers.