Simulates an contemporaneous and temporal network using the method described by Yin and Li (2001)

1 2 | ```
randomGVARmodel(Nvar, probKappaEdge = 0.1, probKappaPositive = 0.5, probBetaEdge = 0.1,
probBetaPositive = 0.5, maxtry = 10, kappaConstant = 1.1)
``` |

`Nvar` |
Number of variables |

`probKappaEdge` |
Probability of an edge in contemporaneous network |

`probKappaPositive` |
Proportion of positive edges in contemporaneous network |

`probBetaEdge` |
Probability of an edge in temporal network |

`probBetaPositive` |
Propotion of positive edges in temporal network |

`maxtry` |
Maximum number of attempts to create a stationairy VAR model |

`kappaConstant` |
The constant used in making kappa positive definite. See Yin and Li (2001) |

The resulting simulated networks can be plotted using the plot method.

A list containing:

`kappa` |
True kappa structure (residual inverse variance-covariance matrix) |

`beta` |
True beta structure |

`PCC` |
True partial contemporaneous correlations |

`PDC` |
True partial temporal correlations |

Sacha Epskamp

Yin, J., & Li, H. (2011). A sparse conditional gaussian graphical model for analysis of genetical genomics data. The annals of applied statistics, 5(4), 2630-2650.

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