smart updates and rolling windows

Brad and List,
	I have implemented quite a number of statistical methods in a 'moving'
way. Most of these implementations rely on common sense transformations,
and a few strategic approximations. What I would really like, and I am
sure Brad would second, is a good reference for these sorts of
calculations. 
	For example, the updating of matrix factorizations is fairly well
researched and documented, but it is not always evident when such
approaches can be applied in a particular situation. Is there a good
collection of real-time algorithms as applied to higher level problems?
Any suggested references would be appreciated. 
	By way of payment, I offer this small calculation that I find useful to
have in my R (Splus actually) toolbox. It generalizes a number of moving
operations, and often provides sufficient speedup over a raw for-loop to
remove the need for further optimization. I won't give the
implementation, just the idea, implementation is a few lines of C. The
function is a moving matrix multiply. The signature is:

mov.mat.mul <- function(X, A)

and the operation is

Y <- mov.mat.mul(x, A)

If x is a length n vector, and A is a m x w matrix then, Y is a n x m
matrix where:

=> Y[i,] = A %*% x[(i-w+1):i]

With the right choice of A one can do moving regressions of all sorts.
Also, doing:

	apply(mov.mat.mul(diag(win), x), 1, my.non.linear.f)

is a nice way to do arbitrary moving functions and get sufficiently good
performance to assess whether further improvement is warranted.  	
	To summarize, I would like to hear about good references for these
sorts of calculations. My own small contribution is to suggest that
moving matrix multiplication is a fundamental operation that is useful
for moving computations.

	Matt