I am still working on improving the computational methods for linear mixed
models, especially with regard to fitting models to large data sets.
 Obviously I will compare the performance of different approaches on
simulated data sets but it is usually much more informative to compare the
methods on real data.

If you have used lmer to fit linear mixed models to large data sets and you
can release your data I would very much appreciate your contacting me.
 Because the purpose is to benchmark various computational methods rather
than demonstrating analysis strategies anonymized data is fine.

All I need is the fixed-effects model matrix, enough information to
generate the random-effects model matrix and the response.  If the random
effects have the form

(1|f) + (1|g) + ...

which I refer to as "simple scalar random effects only",  then I just need
to grouping factors f, g, etc.  If there are vector-valued random-effects
terms, like

(year|student)

then I will need the vectors on the left-hand side as well as those on the
right-hand side.

The simplest form to transfer is a saved R data set, preferrably using the
'xz' compression but whatever works for you is fine by me.  I don't think I
will make these into a package because such a package could be very large.
 I will, however, arrange for some form of public availability of the data.

To me, a "large data set" for linear mixed models is at least in the
hundreds of thousands of responses.  Cases with partially crossed grouping
factors for random effects would be especially appreciated.

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