## 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]
## Obtain regularized precision under optimal penalty
OPT <- optPenalty.LOOCV(X, 10, 30, 10, target = diag(diag(1/covML(X))))
## Check symmetry
## OPT$optPrec is symmetric by definition
## But is not recognized as such due to rounding peculiarities
isSymmetric(OPT$optPrec)
## Symmetrize
symm(OPT$optPrec)
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