Generate precision matrix of nearest-neighbor network following the set-up in Li and Gui (2006) and Lee and Liu (2006).

1 | ```
NeighborOmega(p, sd = 1, knn = 4, norm.type = 2)
``` |

`p` |
dimension of generated precision matrix. |

`sd` |
seed for random number generation. Default is 1. |

`knn` |
sparsity of precision matrix, i.e., matrix is generated from a |

`norm.type` |
normalization methods of generated precision matrix, i.e., |

For a `knn`

nearest-neighbor graph, this function first randomly picks p points from a
unit square and computes all pairwise distances among the points. Then it searches for the knn nearest-neighbors
of each point and a pair of symmetric entries in the precision matrix that has a random chosen value from *[-1, -0.5] U [0.5, 1]*. Finally, to
ensure positive definite property, it normalizes the matrix as *Ω <- Ω + (λ (Ω)+0.2) 1_p* where
*λ (.)* refers to the samllest eigenvalue.

A precision matrix generated from the `knn`

nearest-neighor graph.

Will Wei Sun, Zhaoran Wang, Xiang Lyu, Han Liu, Guang Cheng.

1 2 3 4 5 6 7 8 9 10 | ```
m.vec = c(5,5,5) # dimensionality of a tensor
n = 5 # sample size
knn=4 # sparsity
Omega.true.list = list()
for ( k in 1:length(m.vec)){
Omega.true.list[[k]] = NeighborOmega(m.vec[k],knn=4, sd=k*100,norm.type=2)
}
Omega.true.list # a list of length 3 contains precision matrices from 4-nearnest neighbor graph
``` |

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