[R] Covariance of data with missing values.

[Rolf Turner]

I have a data matrix X (n x k, say) each row of which constitutes an  
observation of
a k-dimensional random variable which I am willing, if not happy, to  
assume to be
Gaussian, with mean ``mu'' and covariance matrix ``Sigma''.  Distinct  
rows of X may
be assumed to correspond to independent realizations of this random  
variable.

Most rows of X (all but 240 out of 6000+ rows) contain one or more  
missing values.
If I am willing to assume that missing entries are missing completely  
at random (MCAR)
then I can estimate the covariance matrix Sigma via maximum  
likelihood, by
employing the EM algorithm.  Or so I believe.

Has this procedure been implemented in R in an accessible form?  I've  
had a bit of a
scrounge through the searching facilities, and have gone through the  
FAQ, and have
found nothing that I could discern to be directly relevant.

Thanks for any pointers that anyone may be able to give.

			cheers,

				Rolf Turner

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