## Obtain some (high-dimensional) data
p = 25
n = 10
set.seed(333)
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]
Cx <- covML(X)
## Obtain regularized precision matrix
P <- ridgeP(Cx, lambda = 10, type = "Alt")
## Obtain sparsified partial correlation matrix
PC0 <- sparsify(P, threshold = "localFDR", FDRcut = .8)
## Obtain adjacency matrix
adjacentMat(PC0$sparsePrecision)
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