I am looking for an R package or function that would estimate the
variance-covariance matrix of a linear mixed model.
I have used pedigreemm and lme4 for simple examples but I would like to
know how to provide my own covariance structure.
E.g. G, the variance-covariance matrix of a lmm model will typically be the
tensor product of a typically small matrix g (e.g. a 2 x2 matrix of 3
variance-covariance parameters to estimate : sigxx,sigxy,sigyy) with of a
known matrix A. I wish to read matrix A from a file, and ask the R function
to estimate g. I am not interested only in the case of diagonal g matrices.

Is anybody aware of an interface e.g. to lme4, that would allow users to
specify and fit their models in such, flexible, way?
Please specify in your answer the input format for A (i.e sparse CSR,
dense, trios, or any) - (g is in dense format).

Note that a package like Jarrod Hadfield's MCMCglmm allows such flexible
definitions. However, I am looking for a non MCMC estimator (e.g. REML).

P.Fullsack
Dalhousie University

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