See hierband
for the problem this is solving. If lamlist
not provided, then grid will be constructed
starting at lambda_max, the smallest value of lam for which the solution (with delta=NULL
) is diagonal.
1 2  hierband.path(Sighat, nlam = 20, flmin = 0.01, lamlist = NULL, w = NULL,
delta = NULL, maxiter = 100, tol = 1e07)

Sighat 
The sample covariance matrix 
nlam 
Number of lambda values to include in grid. 
flmin 
Ratio between the smallest lambda and largest lambda in grid. (Default: 0.01) Decreasing this gives less sparse solutions. 
lamlist 
A grid of lambda values to use. If this is nonNULL, then 
w 

delta 
Lower bound on eigenvalues. If this is NULL (which is default), then no eigenvalue constraint is included. 
maxiter 
Number of iterations of blockwise coordinate descent to perform. 
tol 
Only used when 
Returns a sequence of convex banded estimates of the covariance matrix.
A nrow(Sighat)
bynrow(Sighat)
bynlam
array where P[, , i]
gives the i
th estimate of the covariance matrix.
Grid of lambda values used.
Value of w used.
Value of delta used.
hierband
hierband.cv
1 2 3 4 5 6 7 8 9 10  set.seed(123)
p < 100
n < 50
K < 10
true < ma(p, K)
x < matrix(rnorm(n*p), n, p) %*% true$A
Sighat < cov(x)
path < hierband.path(Sighat)
cv < hierband.cv(path, x)
fit < hierband(Sighat, lam=cv$lam.best)

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