Nothing

```
# generate data from a sparse matrix
# first compute covariance matrix
S = matrix(0.7, nrow = 5, ncol = 5)
for (i in 1:5){
for (j in 1:5){
S[i, j] = S[i, j]^abs(i - j)
}
}
# generate 100 x 5 matrix with rows drawn from iid N_p(0, S)
Z = matrix(rnorm(100*5), nrow = 100, ncol = 5)
out = eigen(S, symmetric = TRUE)
S.sqrt = out$vectors %*% diag(out$values^0.5) %*% t(out$vectors)
X = Z %*% S.sqrt
# calculate sample covariance
(nrow(X) - 1)/nrow(X)*cov(X)
# ridge penalty (no ADMM)
expect_error(RIDGEsigma(X), NA)
expect_warning(RIDGEsigma(X), NA)
# lasso penalty (lam = 0.1)
expect_error(RIDGEsigma(X, lam = 0.1), NA)
expect_warning(RIDGEsigma(X, lam = 0.1), NA)
expect_error(RIDGEsigma(S = S, lam = 0.1), NA)
expect_warning(RIDGEsigma(S = S, lam = 0.1), NA)
# parallel CV
expect_error(RIDGEsigma(X, cores = 2), NA)
expect_warning(RIDGEsigma(X, cores = 2), NA)
# path
expect_error(RIDGEsigma(X, path = TRUE), NA)
expect_warning(RIDGEsigma(X, path = TRUE), NA)
expect_error(RIDGEsigma(S = S, path = TRUE), NA)
expect_warning(RIDGEsigma(S = S, path = TRUE), NA)
```

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