I see that a similar question has been asked in the past, but I am not
totally clear with the in-development status of lme4 if things have changed.

Is there a way to call lmer (and glmer) where I have pre-computed random
effect design matrices and residual covariances?
My design matrices are crossed and don't correspond to any of the easy
formula interfaces that I know about.
I see in the belly of the lmer code where Zt's are stored, but am hesitant
to jump in and change them; I'm hoping someone familiar with the package
has a wrapper for this use-case.
I know how to do this in the "regress" package for LMMs, but would like to
use lme4's superior fast methods for sparse matrices and have something
reasonable for (binary) GLMMs.
I am aware of how to do this in MCMCglmm, but for a particularly
large-scale project have found it unacceptably slow.

Thanks,
C Ryan King
University of Chicago

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