Nothing

```
# DESP/R/DESP_PML.R by A. S. Dalalyan and S. Balmand Copyright (C) 2015-
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License (version 3) as published by
# the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# http://www.r-project.org/Licenses/
#
DESP_PML <-
function(X,B,thresh,kappa,tol,Theta=NULL) {
# estimation of the diagonal of the precision matrix by penalized likelihood minimization, when the true value of B is known or has already been estimated
# the observations of the data matrix X are assumed to have zero mean
# main function
# read the sample size and the number of variables
D = dim(X);
n = D[1]; # n is the sample size
p = D[2]; # p is the dimension
hessian <-
function(v,B,thresh,kappa) {
p = dim(B)[2]; # p is the dimension
H = matrix(0,p,p)
for (i in 1:p){
for (j in 1:p){
if (B[j,i]*B[i,j]>thresh) {
H[i,j] = kappa * 2 * (-B[i,j]) * B[j,i] / (B[i,j]^2 + B[j,i]^2)
}}}
for (j in 1:p){
H[j,j] = 1/v[j]^2
for (j in 1:p){
if (B[j,i]*B[i,j]>thresh) {
H[j,j] = H[j,j] + kappa * 2 * B[i,j]^2 / (B[i,j]^2 + B[j,i]^2)
}}}
return(H)
}
# compute the sample cov matrix
if(is.null(Theta))
{
S = crossprod(X)/n;
}
else
{
S = crossprod(X - Theta %*% MASS::ginv(B))/n;
}
# stepsize computation
maxSV = max(svd(hessian(rep(n^(1/2),p),B,thresh,kappa))$d)
Phi_inv = DESP_PEN_grad(S,B,rep(1,p),kappa,thresh,1/maxSV,tol);
return(Phi_inv);
}
```

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