How can I make R using more than 1 core (8 available) on a Ubuntu Rstudio server ?

Explaining a little bit more; unlike a lot of informatics/machine
learning procedures, the algorithm underlying lme4 is not naturally
parallelizable. There are components that *could* be done in parallel,
but it's not simple.

  If you need faster computation, you could either try Doug's
MixedModels.jl package for Julia, or the glmmTMB package (on CRAN),
which may scale better than glmer for problems with large numbers of
fixed-effect parameters (although my guess is that it's close to a tie
for the problem specs you quote below, unless your fixed effects are
factors with several levels).

  Sometimes installing better-optimized linear algebra libraries or
better-optimized builds of R can help (optimized BLAS or Microsoft's
"R Open"), although likely not in the case of lme4.

  My other comment is that a lot of the computational load of modeling
has to do with running lots of different models, not with how long a
single model takes.  For example,

 - likelihood profiling
 - parametric bootstrapping
 - model comparison and testing via likelihood ratio tests or
information criteria
 - model selection (ugh)

Are all procedures that can be easily parallelized (support for
parallel computation is built-in for the first two).

  cheers
    Ben Bolker

How can I make R using more than 1 core (8 available) on a Ubuntu Rstudio server ?

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