Is there any chance of development ofmultivariate linear mixed models for lme4

The way that I would like to approach this kind of model is to
incorporate the variance-covariance of the multivariate response as a
"pre-whitening" transformation.  In the "Implemenentation" vignette in
the lme4 package I describe the representation of a positive
semi-definite matrix (i.e. a general variance-covariance matrix) as
the product of a diagonal matrix and a unit lower-triangular matrix.
That parameterization could be used for the variance-covariance of the
multivariate response.  (It may be necessary to constrain one of the
diagonal elements of the diagonal matrix to 1 because of the profiling
out of the scalar variance parameter.)

Conditional on those parameters the model matrices and responses could
be "pre-whitened" to a set of independent, constant-variance response
and the corresponding model matrices.  These would then update the
ZXyt slot in the mer2 representation and the optimization could
proceed from there.  In lme we used a "nested" optimization.  I think
I would not recommend doing that here.  I would try to do the
optimization jointly.

I imagine there would need to be another factor in the deviance that
takes into account the variance-covariance structure of the responses.

Generally I would like to regard the what I am now calling the mer2
representation (it will become the mer class later when I have all the
necessary methods programmed) as a building block for models that
extend the univariate linear mixed effects model.  These include the
generalized linear mixed effects model, the nonlinear mixed effects
model, the multivariate linear mixed effects model, ...

Is there any chance of development ofmultivariate linear mixed models for lme4

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