The notion of threading, for those who may not have this background, refers to several instances of a program, in this case, several instances of R, sharing global variables but otherwise running independently. As Bartnik points out, this can make I/O programming easier and clearer; see my Python tutorial, Chapter 4, for a network sockets example, in which the code must deal with situations in which data may come from one of many sockets, but without foreknowledge of which socket will be next. By having a separate process devoted to each socket, but storing the incoming data in a shared variable, the problem is neatly solved (and more conveniently than using nonblocking I/O).
R examples of various sorts are given in the Rdsm package. And as Bartnik also points out, perhaps the most common situation where his or my package might be used is running an R “background job.”
In terms of speedups through parallelization, the results are mixed. See my book on parallel computation for data science. Bartnik’s package conceivably could have lower overhead, making parallelization speedup from threaded R more feasible.
For me, a major piece of unfinished business regarding Rdsm is the use of backing storage, i.e. storing the shared variables on disk. This could help with problems having large memory needs, and may be useful for distributed computation. Rdsm runs on top of bigmemory, which does allow use of backing storage. However, this seems to require a file system that immediately propagates file changes made by one process to visibility by other processes, which didn’t seem to work on ordinary Linux systems, for instance.
As to truly threaded R, my understanding (could need an update) is that the R Core team has vowed “Never!” Too many technical issues.