# Functions to aid in the use and construction of CIDnetworks objects

### Description

Functions to aid in the use and construction of CIDnetworks objects

### Usage

1 2 3 4 5 6 7 | ```
l.diag (nn)
u.diag (nn)
ordinal.maker (vec, cuts=quantile(vec, c(0.25, 0.5, 0.75)))
unwrap.CID.Gibbs (gibbs.out)
mat.cov.to.edge.list.cov (Xmat, n.nodes = dim(Xmat)[1],
arc.list = make.arc.list(n.nodes))
``` |

### Arguments

`nn` |
The number of rows in the square matrix for which we wish to extract the lower or upper diagonal matrix. |

`vec` |
The elements to be divided into ordinal categories. |

`cuts` |
The cut points at which to divide vec into ordinal categories. Default values separate vec into quartiles. |

`gibbs.out` |
The list object of draws from the Gibbs sampler. This re-sorts the object into a matrix form for easier consumption. |

`Xmat` |
A three-dimensional array of covariates, with n.nodes rows and columns. Each slice is a different covariate. |

`n.nodes` |
Number of nodes in network |

`arc.list` |
List of potential edges in network. |

### Details

These functions are included for the convenience of users of CIDnetworks. l.diag and u.diag provide the indices of a matrix to extract the lower and upper diagonal elements. ordinal.maker will turn any numeric vector into a series of ordinal integers for easy use in a CIDnetworks outcome. Xmat converts a sociomatrix-style array of covariates into one that can easily be used by the COV() component.

### Author(s)

A.C. Thomas <act@acthomas.ca>

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