Generate an inverse covariance matrix, covariance matrix, or binary network with hub structure

1 | ```
HubNetwork(p, sparsity, hubnumber, hubsparsity, type = "Gaussian")
``` |

`p` |
The number of features |

`sparsity` |
Sparsity of the network |

`hubnumber` |
The number of hubs in the network |

`hubsparsity` |
Sparsity level within each hub |

`type` |
Type of network. The default value type="Gaussian" generates an inverse covariance matrix. type="covariance" generates a covariance matrix with hubs. type="binary" generates a binary network with hubs. |

`Theta` |
Theta is the generated inverse covariance matrix, covariance matrix, or binary network. |

`hubcol` |
hubcol contains indices for features that are hubs. |

Kean Ming Tan

Tan et al. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.

1 2 3 4 5 6 7 8 9 10 11 | ```
# Generate inverse covariance matrix with 5 hubs
# 30% of the elements within a hub are zero
# 95% of the elements that are not within hub nodes are zero
p <- 100
Theta <- HubNetwork(p,0.95,5,0.3)$Theta
# Generate covariance matrix with 5 hubs with similar structure
Sigma <- HubNetwork(p,0.95,5,0.3,type="covariance")$Theta
# Generate binary network with 2 hubs with p=10
Theta <- HubNetwork(p=10,0.95,2,0.3,type="binary")$Theta
``` |

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