adj_confusion |
Compute confusion matrix for comparing two adjacency matrices |

as.graphNEL |
Convert adjacency matrix to graphNEL object |

average_degree |
Compute average degree for adjacency matrix |

compare |
Compare two tpdag or tskeleton objects |

confusion |
Compute confusion matrix for comparing two adjacency matrices |

corTest |
Test for vanishing partial correlations |

dir_confusion |
Compute confusion matrix for comparing two adjacency matrices |

edges |
List of edges in adjacency matrix |

essgraph2amat |
Convert essential graph to adjacency matrix |

evaluate |
Evaluate adjacency matrix estimation |

evaluate.array |
Evaluate adjacency matrix estimation |

evaluate.matrix |
Evaluate adjacency matrix estimation |

evaluate.tamat |
Evaluate adjacency matrix estimation |

F1 |
F1 score |

FDR |
False Discovery Rate |

FOR |
False Omission Rate |

G1 |
G1 score |

gausCorScore |
Gaussian L0 score computed on correlation matrix |

graph2amat |
Convert graphNEL object to adjacency matrix |

is_cpdag |
Check for CPDAG |

is_pdag |
Check for PDAG |

maketikz |
Generate Latex tikz code for plotting a temporal DAG or PDAG. |

maxnedges |
Compute maximal number of edges for graph |

nDAGs |
Number of different DAGs |

nedges |
Number of edges in adjacency matrix |

NPV |
Negative predictive value |

plot.tamat |
Plot adjacency matrix with order information |

plot.tpdag |
Plot temporal partially directed acyclic graph (TPDAG) |

plot.tskeleton |
Plot temporal skeleton |

plotTempoMech |
Plot temporal data generating mechanism |

precision |
Precision |

probmat2amat |
Convert a matrix of probabilities into an adjacency matrix |

recall |
Recall |

regTest |
Regression-based information loss test |

shd |
Structural hamming distance between adjacency matrices |

simDAG |
Simulate a random DAG |

simGausFromDAG |
Simulate Gaussian data according to DAG |

specificity |
Specificity |

tamat |
Make a temporal adjacency matrix |

tpc |
Perform causal discovery using the temporal PC algorithm (TPC) |

tpcExample |
Simulated data example |