I am trying to fit a linear mixed model of the form

y_ij = X_ij \beta + delta_i + e_ij

where e_ij ~N(0,s^2_ij) with s_ij known
and delta_i~N(0,tau^2)

I looked at the ecme routine in package:pan, but this routine
does not allow for different Vi (variance covariance matrix of
the e_i vector) matrices for each cluster.
 
Is there an easy way to fit this model in R or should I bite the
bullet and code the likelihood functions ?
 
Also, is there an easy way to fit a Bayesian version of this ? Again
there is mgibbs.lmm but it also does not allow easily for a different
Vi matrix for each cluster/.

Thanks,

Benn



		
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