From Manuel Schiller:
in CholeskyDecomp (SMatrix package), give access to the Cholesky
	decomposition itself, i.e. the matrix L from M = L L^T; this is
	useful if one needs to decorrelate a number of random variables
	for which the covariance matrix is known (decompose covariance
	M, apply L to the vector of random variables to obtain a new
	vector or uncorrelated random variables with unit variance)