I am running rstanarm and loo in RStudio 3.5.1 on a virtual desktop running Windows 10 with 12 (virtual) cores and 64 GBytes of main memory. options(mc.cores = parallel::detectCores()) is set, and I have confirmed that options()$mc.cores = 12. In I have computed a hierarchical repeated measures model using rstanarm::lmer with 2 or 3 main effects, 128 individuals, and about 2000 observations. The model converges well.
Then I ran loo 2.0.0 on the model. The function took over 3 hours to return. This did not seem consonant with the loo documentation that suggested that it was a rapid method to calculate elpd. So I reran the command and followed use of memory and cpu using TaskManager. It appears that the function is only using one core, as the fraction of cpu use is never higher than 8.3%. I retried this explicitly using the parameter cores = 12. The result was the same, as was the result running the function in native R. I have also repeated the experiment on my personal laptop. Same result.
Either I am making some sort of error that I can’t figure out, or there may be a bug in loo(). Have any of you had this problem? What might I be doing wrong? Thanks in advance to anyone that can help me with this.