AIC/BIC regularization parameter selection
Finds in a computationally fast algorithm the average square correlation magnitude for every variable of a dataset.
1  cor2mean(mat)

mat 
p \times n matrix with the pvariate dataset. 
It is especially suitable for high dimensions. For instance it handles well dimensions of order of thousands.
The average square correlation magnitude of the sample correlation matrix (including the diagonal) for every variable in mat
.
Mayer, Claus, Adria Caballe and Natalia Bochkina.
To come
cor2mean.adj
for adjusted average square correlation magnitude.
1 2 3  EX1 < pcorSimulator(nobs = 50, nclusters= 3, nnodesxcluster = c(100,30,50),
pattern = "powerLaw", plus = 0)
corsEX1 < cor2mean(t(EX1$y))

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.