Description Usage Arguments Value

Covariate Assisted Spectral Clustering

1 2 3 | ```
casc(adjMat, covMat, nBlocks, nPoints = 100, method = "regLaplacian",
rowNorm = F, enhancedTuning = F, center = F, verbose = F,
assortative = F, randStarts = 10, epsilon = 0.05)
``` |

`adjMat` |
An adjacency matrix |

`covMat` |
A covariate matrix |

`nBlocks` |
The number of clusters |

`nPoints` |
Number of iterations to find the optimal tuning parameter. |

`method` |
The form of the adjacency matrix to be used. |

`rowNorm` |
True if row normalization should be done before running kmeans. |

`enhancedTuning` |
If true, then the enhanced tuning procedure is used. |

`center` |
A boolean indicating if the covariate matrix columns should be centered. |

`verbose` |
A boolean indicating if casc output should include eigendecomposition. |

`assortative` |
A boolean indicating if the assortative version of casc should be used. |

`randStarts` |
Number of random restarts for kmeans. |

`epsilon` |
A threshold for identifying subspace discontinuities. |

A list with node cluster assignments, the the value of the tuning parameter used, the within cluster sum of squares, and the eigengap.

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