Description Usage Arguments Details Value Author(s) References Examples

Infer network structures from multiple datasets with mixed types of variables and edge restrictions option available.

1 |

` data ` |
a list of |

` ALPHA1 ` |
The significance level of correlation screening. In general, a high significance level of correlation screening will lead to
a slightly large separator set |

` ALPHA2 ` |
The significance level of |

`restrict` |
Should edge restriction applied? (logical). If |

`parallel` |
Should parallelization be used? (logical), default is |

`nCPUs` |
Number of cores used for parallelization. Recommend to be equal to the number of datasets. |

This is the function that can jointly estimate multiple graphical models with mixed types of data and also consider the edge restriction scenarios. The method has three novelties: First, the proposed method resolves the conditional independence information using a *p*-learning algorithm and therefore can be applied to the mixed types of random variables. Second, the proposed method can construct networks with restricted edges determined by some preliminary knowledges. Third, the proposed method involves a Fast Bayesian joint estimation method which works on edge-wise scores and can achieve both fast and accurate integration performance for constructing multiple networks. See Jia and Liang (2018).

A list of three elements:

`A` |
An array of multiple adjacency matrices of networks which is a |

`score.sep` |
Separately estimated |

`score.joint` |
Estimated integrative |

Bochao Jiajbc409@gmail.com and Faming Liang

Jia, B., and Liang, F. (2018) Joint Estimation of Restricted Mixed Graphical Models. manuscript.

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