This document is an automatically generated cross reference for the ergm model terms from the stanet project. The source for this data and additional descriptions are in the `?ergm.terms`

help file or the ergm manual.

It is possible to search the `ergm-terms`

help page and search for specific categories of terms using the `search.ergmTerms`

command. For example to find all the terms that mention 'triangle' in their description:

```
search.ergmTerms(keyword='triangle')
```

```
## Found 10 matching ergm terms:
## ctriple(attr=NULL, diff=FALSE, levels=NULL)
## Cyclic triples
##
## ctriad()
## Cyclic triples
##
## cycle(k)
## Cycles
##
## localtriangle(x)
## Triangles within neighborhoods
##
## opentriad()
## Open triads
##
## threetrail(keep=NULL, levels=NULL)
## Three-trails
##
## triangle(attr=NULL, diff=FALSE, levels=NULL)
## Triangles
##
## tripercent(attr=NULL, diff=FALSE, levels=NULL)
## Triangle percentage
##
## ttriple(attr=NULL, diff=FALSE, levels=NULL)
## Transitive triples
##
## ttriad()
## Transitive triples
```

Or to find all of the dyad-independent bipartite terms:

```
search.ergmTerms(categories=c('bipartite','dyad-independent'))
```

```
## Found 15 matching ergm terms:
## b1cov(attr)
## Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
##
## b1cov(attr, form="sum")
## Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
##
## b1factor(attr, base=1, levels=-1)
## Factor attribute effect for the first mode in a bipartite (aka two-mode) network
##
## b1factor(attr, base=1, levels=-1, form="sum")
## Factor attribute effect for the first mode in a bipartite (aka two-mode) network
##
## b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1,)
## Nodal attribute-based homophily effect for the first mode in a bipartite (aka two-mode) network
##
## b1sociality(nodes=-1)
## Degree
##
## b1sociality(nodes=-1, form="sum")
## Degree
##
## b2cov(attr)
## Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
##
## b2cov(attr, form="sum")
## Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
##
## b2factor(attr, base=1, levels=-1)
## Factor attribute effect for the second mode in a bipartite (aka two-mode) network
##
## b2factor(attr, base=1, levels=-1, form="sum")
## Factor attribute effect for the second mode in a bipartite (aka two-mode) network
##
## b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1,)
## Nodal attribute-based homophily effect for the second mode in a bipartite (aka two-mode) network
##
## b2sociality(nodes=-1)
## Degree
##
## b2sociality(nodes=-1, form="sum")
## Degree
##
## diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum")
## Difference
```

For convenience, this table lists a subset of the most commonly-used ergm terms and categories.

Term name | binary | valued | directed | undirected | bipartite | dyad-independent |
---|---|---|---|---|---|---|

absdiff | ✔ | ✔ | ✔ | ✔ | ||

b1cov | ✔ | ✔ | ✔ | ✔ | ||

b1cov | ✔ | ✔ | ✔ | ✔ | ||

b1degree | ✔ | ✔ | ✔ | |||

b1factor | ✔ | ✔ | ✔ | ✔ | ||

b1factor | ✔ | ✔ | ✔ | ✔ | ||

b1nodematch | ✔ | ✔ | ✔ | ✔ | ||

b2concurrent | ✔ | ✔ | ✔ | |||

b2cov | ✔ | ✔ | ✔ | ✔ | ||

b2cov | ✔ | ✔ | ✔ | ✔ | ||

b2degree | ✔ | ✔ | ✔ | |||

b2factor | ✔ | ✔ | ✔ | ✔ | ||

b2factor | ✔ | ✔ | ✔ | ✔ | ||

b2nodematch | ✔ | ✔ | ✔ | ✔ | ||

degree | ✔ | ✔ | ||||

diff | ✔ | ✔ | ✔ | ✔ | ||

edgecov | ✔ | ✔ | ✔ | ✔ | ||

edges | ✔ | ✔ | ✔ | ✔ | ✔ | |

gwdegree | ✔ | ✔ | ||||

gwesp | ✔ | ✔ | ✔ | |||

idegree | ✔ | ✔ | ||||

isolates | ✔ | ✔ | ✔ | |||

mm | ✔ | ✔ | ✔ | ✔ | ||

mm | ✔ | ✔ | ✔ | ✔ | ||

mutual | ✔ | ✔ | ||||

nodecov | ✔ | ✔ | ✔ | ✔ | ||

nodefactor | ✔ | ✔ | ✔ | ✔ | ||

nodeicov | ✔ | ✔ | ||||

nodeifactor | ✔ | ✔ | ✔ | |||

nodematch | ✔ | ✔ | ✔ | ✔ | ||

nodemix | ✔ | ✔ | ✔ | ✔ | ||

odegree | ✔ | ✔ | ||||

triangle | ✔ | ✔ | ✔ |

This table lists the complete set of terms available in the ergm package. In HTML versions, clicking on a term name will jump to its definition.

Term name | binary | dyad-independent | frequently-used | directed | undirected | quantitative nodal attribute | valued | categorical nodal attribute | curved | triad-related | bipartite | quantitative nodalattribute | deprecated | non-negative |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

absdiff | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

absdiff | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

absdiffcat | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

absdiffcat | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

altkstar | ✔ | ✔ | ✔ | ✔ | ||||||||||

asymmetric | ✔ | ✔ | ✔ | ✔ | ||||||||||

atleast | ✔ | ✔ | ✔ | ✔ | ||||||||||

atmost | ✔ | ✔ | ✔ | ✔ | ||||||||||

b1concurrent | ✔ | ✔ | ✔ | ✔ | ||||||||||

b1cov | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b1cov | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b1degrange | ✔ | ✔ | ✔ | |||||||||||

b1degree | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

b1factor | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b1factor | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b1mindegree | ✔ | ✔ | ✔ | |||||||||||

b1nodematch | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b1sociality | ✔ | ✔ | ✔ | ✔ | ||||||||||

b1sociality | ✔ | ✔ | ✔ | ✔ | ||||||||||

b1star | ✔ | ✔ | ✔ | ✔ | ||||||||||

b1starmix | ✔ | ✔ | ✔ | ✔ | ||||||||||

b1twostar | ✔ | ✔ | ✔ | ✔ | ||||||||||

b2concurrent | ✔ | ✔ | ✔ | ✔ | ||||||||||

b2cov | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b2cov | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b2degrange | ✔ | ✔ | ✔ | |||||||||||

b2degree | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

b2factor | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b2factor | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b2mindegree | ✔ | ✔ | ✔ | |||||||||||

b2nodematch | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

b2sociality | ✔ | ✔ | ✔ | ✔ | ||||||||||

b2sociality | ✔ | ✔ | ✔ | ✔ | ||||||||||

b2star | ✔ | ✔ | ✔ | ✔ | ||||||||||

b2starmix | ✔ | ✔ | ✔ | ✔ | ||||||||||

b2twostar | ✔ | ✔ | ✔ | ✔ | ||||||||||

balance | ✔ | ✔ | ✔ | ✔ | ||||||||||

coincidence | ✔ | ✔ | ✔ | |||||||||||

concurrent | ✔ | ✔ | ✔ | |||||||||||

concurrentties | ✔ | ✔ | ✔ | |||||||||||

ctriple | ✔ | ✔ | ✔ | ✔ | ||||||||||

ctriad | ✔ | ✔ | ✔ | ✔ | ||||||||||

cycle | ✔ | ✔ | ✔ | |||||||||||

cyclicalties | ✔ | ✔ | ||||||||||||

cyclicalties | ✔ | ✔ | ✔ | |||||||||||

cyclicalweights | ✔ | ✔ | ✔ | |||||||||||

ddsp | ✔ | ✔ | ||||||||||||

degrange | ✔ | ✔ | ✔ | |||||||||||

degree | ✔ | ✔ | ✔ | ✔ | ||||||||||

degree1.5 | ✔ | ✔ | ||||||||||||

degreepopularity | ✔ | ✔ | ✔ | |||||||||||

degcrossprod | ✔ | ✔ | ||||||||||||

degcor | ✔ | ✔ | ||||||||||||

density | ✔ | ✔ | ✔ | ✔ | ||||||||||

diff | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

diff | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

desp | ✔ | ✔ | ||||||||||||

dgwdsp | ✔ | ✔ | ||||||||||||

dgwesp | ✔ | ✔ | ||||||||||||

dgwnsp | ✔ | ✔ | ||||||||||||

dnsp | ✔ | ✔ | ||||||||||||

dsp | ✔ | ✔ | ✔ | |||||||||||

dyadcov | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

edgecov | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

edgecov | ✔ | ✔ | ✔ | ✔ | ||||||||||

edges | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

nonzero | ✔ | ✔ | ✔ | ✔ | ||||||||||

esp | ✔ | ✔ | ✔ | |||||||||||

equalto | ✔ | ✔ | ✔ | ✔ | ||||||||||

greaterthan | ✔ | ✔ | ✔ | ✔ | ||||||||||

gwb1degree | ✔ | ✔ | ✔ | ✔ | ||||||||||

gwb2degree | ✔ | ✔ | ✔ | ✔ | ||||||||||

gwdegree | ✔ | ✔ | ✔ | ✔ | ||||||||||

gwdsp | ✔ | ✔ | ✔ | ✔ | ||||||||||

gwesp | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

gwidegree | ✔ | ✔ | ✔ | |||||||||||

gwnsp | ✔ | ✔ | ✔ | ✔ | ||||||||||

gwodegree | ✔ | ✔ | ✔ | |||||||||||

hamming | ✔ | ✔ | ✔ | ✔ | ||||||||||

hammingmix | ✔ | ✔ | ✔ | |||||||||||

idegrange | ✔ | ✔ | ✔ | |||||||||||

idegree | ✔ | ✔ | ✔ | ✔ | ||||||||||

idegree1.5 | ✔ | ✔ | ||||||||||||

idegreepopularity | ✔ | ✔ | ✔ | |||||||||||

ininterval | ✔ | ✔ | ✔ | ✔ | ||||||||||

intransitive | ✔ | ✔ | ✔ | |||||||||||

isolates | ✔ | ✔ | ✔ | ✔ | ||||||||||

istar | ✔ | ✔ | ✔ | |||||||||||

kstar | ✔ | ✔ | ✔ | |||||||||||

smallerthan | ✔ | ✔ | ✔ | ✔ | ||||||||||

localtriangle | ✔ | ✔ | ✔ | ✔ | ||||||||||

m2star | ✔ | ✔ | ||||||||||||

meandeg | ✔ | ✔ | ✔ | ✔ | ||||||||||

mm | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

mm | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

mutual | ✔ | ✔ | ✔ | |||||||||||

mutual | ✔ | ✔ | ||||||||||||

nearsimmelian | ✔ | ✔ | ✔ | |||||||||||

nodecov | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

nodecov | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

nodemain | ✔ | ✔ | ✔ | |||||||||||

nodecovar | ✔ | ✔ | ✔ | ✔ | ||||||||||

nodefactor | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

nodefactor | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

nodeicov | ✔ | ✔ | ✔ | ✔ | ||||||||||

nodeicov | ✔ | ✔ | ✔ | |||||||||||

nodeicovar | ✔ | ✔ | ✔ | |||||||||||

nodeifactor | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

nodeifactor | ✔ | ✔ | ✔ | ✔ | ||||||||||

nodeisqrtcovar | ✔ | ✔ | ✔ | ✔ | ||||||||||

nodematch | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

nodematch | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

match | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

nodemix | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

nodemix | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

nodeocov | ✔ | ✔ | ✔ | ✔ | ||||||||||

nodeocov | ✔ | ✔ | ✔ | |||||||||||

nodeocovar | ✔ | ✔ | ✔ | |||||||||||

nodeofactor | ✔ | ✔ | ✔ | ✔ | ||||||||||

nodeofactor | ✔ | ✔ | ✔ | ✔ | ||||||||||

nodeosqrtcovar | ✔ | ✔ | ✔ | |||||||||||

nodesqrtcovar | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

nsp | ✔ | ✔ | ✔ | |||||||||||

odegrange | ✔ | ✔ | ✔ | |||||||||||

odegree | ✔ | ✔ | ✔ | ✔ | ||||||||||

odegree1.5 | ✔ | ✔ | ||||||||||||

odegreepopularity | ✔ | ✔ | ✔ | |||||||||||

opentriad | ✔ | ✔ | ✔ | |||||||||||

ostar | ✔ | ✔ | ✔ | |||||||||||

receiver | ✔ | ✔ | ✔ | |||||||||||

receiver | ✔ | ✔ | ✔ | |||||||||||

sender | ✔ | ✔ | ✔ | |||||||||||

sender | ✔ | ✔ | ✔ | |||||||||||

simmelian | ✔ | ✔ | ✔ | |||||||||||

simmelianties | ✔ | ✔ | ✔ | |||||||||||

smalldiff | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

sociality | ✔ | ✔ | ✔ | ✔ | ||||||||||

sociality | ✔ | ✔ | ✔ | ✔ | ||||||||||

sum | ✔ | ✔ | ✔ | |||||||||||

threetrail | ✔ | ✔ | ✔ | |||||||||||

transitive | ✔ | ✔ | ✔ | |||||||||||

transitiveties | ✔ | ✔ | ✔ | ✔ | ||||||||||

transitiveties | ✔ | ✔ | ✔ | ✔ | ||||||||||

transitiveweights | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||

triadcensus | ✔ | ✔ | ✔ | ✔ | ||||||||||

triangle | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||

tripercent | ✔ | ✔ | ✔ | ✔ | ||||||||||

ttriple | ✔ | ✔ | ✔ | ✔ | ||||||||||

ttriad | ✔ | ✔ | ✔ | ✔ | ||||||||||

twopath | ✔ | ✔ | ✔ |

This table lists full definitions for all of the terms along with their tags. Note that some terms may have multiple versions (e.g. valued vs. binary) with slightly different arguments and will be listed more than once with the same definition.

>Description | Categories |
---|---|

absdiff(attr, pow=1)
Note that | binary, dyad-independent, frequently-used, directed, undirected, quantitative nodal attribute |

absdiff(attr, pow=1, form =“sum”)
Note that | valued, dyad-independent, directed, undirected, quantitative nodal attribute |

absdiffcat(attr, base=NULL, levels=NULL)
The argument | binary, dyad-independent, directed, undirected, categorical nodal attribute |

absdiffcat(attr, base=NULL, levels=NULL, form=“sum”)
The argument | valued, dyad-independent, directed, undirected, categorical nodal attribute |

altkstar(lambda, fixed=FALSE)
| binary, undirected, curved, categorical nodal attribute |

asymmetric(attr=NULL, diff=FALSE, keep=NULL, levels=NULL)
The argument | binary, directed, dyad-independent, triad-related |

atleast(threshold=0)
| valued, directed, undirected, dyad-independent |

atmost(threshold=0)
| valued, directed, undirected, dyad-independent |

b1concurrent(by=NULL, levels=NULL)
| binary, bipartite, undirected, categorical nodal attribute |

b1cov(attr)
Note that | binary, undirected, bipartite, dyad-independent, quantitative nodalattribute, frequently-used |

b1cov(attr, form=“sum”)
| valued, undirected, bipartite, dyad-independent, quantitative nodal attribute, frequently-used |

b1degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL)
This term can only be used with bipartite networks; for directed networks see | binary, bipartite, undirected |

b1degree(d, by=NULL, levels=NULL)
This term can only be used with undirected bipartite networks. | binary, bipartite, undirected, categorical nodal attribute, frequently-used |

b1factor(attr, base=1, levels=-1)
The optional To include all attribute values is usually not a good idea, because the sum of all such statistics equals the number of edges and hence a linear dependency would arise in any model also including The argument This term can only be used with undirected bipartite networks. | binary, bipartite, undirected, dyad-independent, frequently-used, categorical nodal attribute |

b1factor(attr, base=1, levels=-1, form=“sum”)
The optional To include all attribute values is usually not a good idea, because the sum of all such statistics equals the number of edges and hence a linear dependency would arise in any model also including
This term can only be used with undirected bipartite networks. | valued, bipartite, undirected, dyad-independent, frequently-used, categorical nodal attribute |

b1mindegree(d)
This term can only be used with undirected bipartite networks. | binary, bipartite, undirected |

b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1,)
The argument If an | binary, bipartite, undirected, dyad-independent, categorical nodal attribute, frequently-used |

b1sociality(nodes=-1)
| binary, bipartite, undirected, dyad-independent |

b1sociality(nodes=-1, form=“sum”)
| valued, bipartite, undirected, dyad-independent |

b1star(k, attr=NULL, levels=NULL)
| binary, bipartite, undirected, categorical nodal attribute |

b1starmix(k, attrname, base=NULL, diff=TRUE, levels=NULL)
The optional
| binary, bipartite, undirected, categorical nodal attribute |

b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL)
The argument | binary, bipartite, undirected, categorical nodal attribute |

b2concurrent(by=NULL)
This term can only be used with undirected bipartite networks. | binary, bipartite, undirected, frequently-used |

b2cov(attr)
| binary, undirected, bipartite, dyad-independent, quantitative nodal attribute, frequently-used |

b2cov(attr, form=“sum”)
| valued, undirected, bipartite, dyad-independent, quantitative nodal attribute, frequently-used |

b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL)
This term can only be used with bipartite networks; for directed networks see | binary, bipartite, undirected |

b2degree(d, by=NULL)
This term can only be used with undirected bipartite networks. | binary, bipartite, undirected, categorical nodal attribute, frequently-used |

b2factor(attr, base=1, levels=-1)
The optional To include all attribute values is usually not a good idea, because the sum of all such statistics equals the number of edges and hence a linear dependency would arise in any model also including
This term can only be used with undirected bipartite networks. | binary, bipartite, undirected, dyad-independent, categorical nodal attribute, frequently-used |

b2factor(attr, base=1, levels=-1, form=“sum”)
The optional
This term can only be used with undirected bipartite networks. | valued, bipartite, undirected, dyad-independent, categorical nodal attribute, frequently-used |

b2mindegree(d)
This term can only be used with undirected bipartite networks. | binary, bipartite, undirected |

b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1,)
The argument If an This term can only be used with undirected bipartite networks. | binary, bipartite, undirected, dyad-independent, categorical nodal attribute, frequently-used |

b2sociality(nodes=-1)
| binary, bipartite, undirected, dyad-independent |

b2sociality(nodes=-1, form=“sum”)
| valued, bipartite, undirected, dyad-independent |

b2star(k, attr=NULL, levels=NULL)
| binary, bipartite, undirected, categorical nodal attribute |

b2starmix(k, attrname, base=NULL, diff=TRUE, levels=NULL)
| binary, bipartite, undirected, categorical nodal attribute |

b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL)
| binary, bipartite, undirected, categorical nodal attribute |

balance()
| binary, triad-related, directed, undirected |

coincidence(levels=NULL,active=0)
| binary, bipartite, undirected |

concurrent(by=NULL, levels=NULL)
| binary, undirected, categorical nodal attribute |

concurrentties(by=NULL, levels=NULL)
| binary, undirected, categorical nodal attribute |

ctriple(attr=NULL, diff=FALSE, levels=NULL)
| binary, directed, triad-related, categorical nodal attribute |

ctriad()
| binary, directed, triad-related, categorical nodal attribute |

cycle(k)
| binary, directed, undirected |

cyclicalties(attr=NULL, levels=NULL)
| binary, directed |

cyclicalties(threshold=0)
| valued, directed, undirected |

cyclicalweights(twopath=“min”,combine=“max”,affect=“min”)
| valued, directed, undirected |

ddsp(d, type=“OTP”)
While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the - Outgoing Two-path (
`"OTP"` ) vertex *k*is an OTP shared partner of ordered pair*(i,j)*iff*i->k->j*. Also known as “transitive shared partner”.- Incoming Two-path (
`"ITP"` ) vertex *k*is an ITP shared partner of ordered pair*(i,j)*iff*j->k->i*. Also known as “cyclical shared partner”- Outgoing Shared Partner (
`"OSP"` ) vertex *k*is an OSP shared partner of ordered pair*(i,j)*iff*i->k, j->k*.- Incoming Shared Partner (
`"ISP"` ) vertex *k*is an ISP shared partner of ordered pair*(i,j)*iff*k->i, k->j*.
By default, outgoing two-paths ( | binary, directed |

degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL)
This term can only be used with undirected networks; for directed networks see | binary, undirected, categorical nodal attribute |

degree(d, by=NULL, homophily=FALSE, levels=NULL)
| binary, undirected, categorical nodal attribute, frequently-used |

degree1.5()
| binary, undirected |

degreepopularity()
| binary, undirected, deprecated |

degcrossprod()
| binary, undirected |

degcor()
| binary, undirected |

density()
| binary, dyad-independent, directed, undirected |

diff(attr, pow=1, dir=“t-h”, sign.action=“identity”)
If The following `"identity"` (the default)no transformation of the difference regardless of sign `"abs"` absolute value of the difference: equivalent to the `absdiff` term`"posonly"` positive differences are kept, negative differences are replaced by 0 `"negonly"` negative differences are kept, positive differences are replaced by 0
Note that this term may not be meaningful for unipartite undirected networks unless | binary, dyad-independent, frequently-used, directed, undirected, quantitative nodal attribute |

diff(attr, pow=1, dir=“t-h”, sign.action=“identity”, form =“sum”)
If The following `"identity"` (the default)no transformation of the difference regardless of sign `"abs"` absolute value of the difference: equivalent to the `absdiff` term`"posonly"` positive differences are kept, negative differences are replaced by 0 `"negonly"` negative differences are kept, positive differences are replaced by 0
Note that this term may not be meaningful for unipartite undirected networks unless | valued, dyad-independent, directed, undirected, bipartite, quantitative nodal attribute |

desp(d, type=“OTP”)
While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the - Outgoing Two-path (
`"OTP"` ) vertex *k*is an OTP shared partner of ordered pair*(i,j)*iff*i->k->j*. Also known as “transitive shared partner”.- Incoming Two-path (
`"ITP"` ) vertex *k*is an ITP shared partner of ordered pair*(i,j)*iff*j->k->i*. Also known as “cyclical shared partner”- Outgoing Shared Partner (
`"OSP"` ) vertex *k*is an OSP shared partner of ordered pair*(i,j)*iff*i->k, j->k*.- Incoming Shared Partner (
`"ISP"` ) vertex *k*is an ISP shared partner of ordered pair*(i,j)*iff*k->i, k->j*.
By default, outgoing two-paths ( | binary, directed |

dgwdsp(decay=0, fixed=FALSE, cutoff=30, type=“OTP”)
While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the - Outgoing Two-path (
`"OTP"` ) vertex *k*is an OTP shared partner of ordered pair*(i,j)*iff*i->k->j*. Also known as “transitive shared partner”.- Incoming Two-path (
`"ITP"` ) vertex *k*is an ITP shared partner of ordered pair*(i,j)*iff*j->k->i*. Also known as “cyclical shared partner”- Outgoing Shared Partner (
`"OSP"` ) vertex *k*is an OSP shared partner of ordered pair*(i,j)*iff*i->k, j->k*.- Incoming Shared Partner (
`"ISP"` ) vertex *k*is an ISP shared partner of ordered pair*(i,j)*iff*k->i, k->j*.
By default, outgoing two-paths ( The optional argument | binary, directed |

dgwesp(decay=0, fixed=FALSE, cutoff=30, type=“OTP”)
- Outgoing Two-path (
`"OTP"` ) *k*is an OTP shared partner of ordered pair*(i,j)*iff*i->k->j*. Also known as “transitive shared partner”.- Incoming Two-path (
`"ITP"` ) *k*is an ITP shared partner of ordered pair*(i,j)*iff*j->k->i*. Also known as “cyclical shared partner”- Outgoing Shared Partner (
`"OSP"` ) vertex *k*is an OSP shared partner of ordered pair*(i,j)*iff*i->k, j->k*.- Incoming Shared Partner (
`"ISP"` ) vertex *k*is an ISP shared partner of ordered pair*(i,j)*iff*k->i, k->j*.
The optional argument | binary, directed |

dgwnsp(decay=0, fixed=FALSE, cutoff=30, type=“OTP”)
- Outgoing Two-path (
`"OTP"` ) *k*is an OTP shared partner of ordered pair*(i,j)*iff*i->k->j*. Also known as “transitive shared partner”.- Incoming Two-path (
`"ITP"` ) *k*is an ITP shared partner of ordered pair*(i,j)*iff*j->k->i*. Also known as “cyclical shared partner”- Outgoing Shared Partner (
`"OSP"` ) vertex *k*is an OSP shared partner of ordered pair*(i,j)*iff*i->k, j->k*.- Incoming Shared Partner (
`"ISP"` ) vertex *k*is an ISP shared partner of ordered pair*(i,j)*iff*k->i, k->j*.
The optional argument | binary, directed |

dnsp(d, type=“OTP”)
- Outgoing Two-path (
`"OTP"` ) *k*is an OTP shared partner of ordered pair*(i,j)*iff*i->k->j*. Also known as “transitive shared partner”.- Incoming Two-path (
`"ITP"` ) *k*is an ITP shared partner of ordered pair*(i,j)*iff*j->k->i*. Also known as “cyclical shared partner”- Outgoing Shared Partner (
`"OSP"` ) vertex *k*is an OSP shared partner of ordered pair*(i,j)*iff*i->k, j->k*.- Incoming Shared Partner (
`"ISP"` ) vertex *k*is an ISP shared partner of ordered pair*(i,j)*iff*k->i, k->j*.
| binary, directed |

dsp(d)
| binary, directed, undirected |

dyadcov(x, attrname=NULL)
| binary, dyad-independent, directed, undirected, categorical nodal attribute |

edgecov(x, attrname=NULL)
| binary, dyad-independent, directed, undirected, frequently-used |

edgecov(x,)
| valued, directed, undirected, dyad-independent |

edges()
| binary, valued, dyad-independent, directed, undirected, frequently-used |

nonzero()
| valued, directed, undirected, dyad-independent |

esp(d)
| binary, directed, undirected |

equalto(value=0, tolerance=0)
| valued, directed, undirected, dyad-independent |

greaterthan(threshold=0)
| valued, directed, undirected, dyad-independent |

gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL)
The optional argument If | binary, bipartite, undirected, curved |

gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL)
The optional argument If | binary, bipartite, undirected, curved |

gwdegree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL)
The optional argument If | binary, undirected, curved, frequently-used |

gwdsp(decay=0, fixed=FALSE, cutoff=30)
The optional argument | binary, directed, undirected, curved |

gwesp(decay=0, fixed=FALSE, cutoff=30)
The optional argument | binary, frequently-used, directed, undirected, curved |

gwidegree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL)
If | binary, directed, curved |

gwnsp(decay=0, fixed=FALSE, cutoff=30)
The optional argument | binary, directed, undirected, curved |

gwodegree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL)
If | binary, directed, curved |

hamming(x, cov, attrname=NULL)
| binary, dyad-independent, directed, undirected |

hammingmix(attr, x, base=NULL, levels=NULL, levels2=NULL)
The argument This term can only be used with directed networks. | binary, directed, dyad-independent |

idegrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL)
This term can only be used with directed networks; for undirected networks (bipartite and not) see | binary, directed, categorical nodal attribute |

idegree(d, by=NULL, homophily=FALSE, levels=NULL)
| binary, directed, categorical nodal attribute, frequently-used |

idegree1.5()
| binary, directed |

idegreepopularity()
| binary, directed, deprecated |

ininterval(lower=-Inf, upper=+Inf, open=)
| valued, directed, undirected, dyad-independent |

intransitive()
| binary, directed, triad-related |

isolates()
| binary, directed, undirected, frequently-used |

istar(k, attr=NULL, levels=NULL)
| binary, directed, categorical nodal attribute |

kstar(k, attr=NULL, levels=NULL)
| binary, undirected, categorical nodal attribute |

smallerthan(threshold=0)
| valued, directed, undirected, dyad-independent |

localtriangle(x)
| binary, triad-related, directed, undirected |

m2star()
| binary, directed |

meandeg()
| binary, dyad-independent, directed, undirected |

mm(attrs, levels=NULL, levels2=NULL)
| binary, dyad-independent, frequently-used, directed, undirected, categorical nodal attribute |

mm(attrs, levels=NULL, levels2=NULL, form=“sum”)
| valued, dyad-independent, frequently-used, directed, undirected, categorical nodal attribute |

mutual(same=NULL, by=NULL, diff=FALSE, keep=NULL, levels=NULL)
This term can only be used with directed networks. The binary version also has the following capabilities: if the optional
| binary, directed, frequently-used |

mutual(form=“min”,threshold=0)
This term can only be used with directed networks. The binary version also has the following capabilities: if the optional
| valued, directed |

nearsimmelian()
| binary, directed, triad-related |

nodecov(attr)
| binary, dyad-independent, frequently-used, directed, undirected, quantitative nodal attribute |

nodecov(attr, form=“sum”)
| valued, dyad-independent, directed, undirected, quantitative nodal attribute |

nodemain()
| binary, directed, undirected |

nodecovar()
| valued, directed, undirected, quantitative nodal attribute |

nodefactor(attr, base=1, levels=-1)
The optional
| binary, dyad-independent, directed, undirected, categorical nodal attribute, frequently-used |

nodefactor(attr, base=1, levels=-1, form=“sum”)
The optional
| dyad-independent, valued, directed, undirected, categorical nodal attribute |

nodeicov(attr)
| binary, directed, quantitative nodal attribute, frequently-used |

nodeicov(attr, form=“sum”)
| valued, directed, quantitative nodal attribute |

nodeicovar()
| valued, directed, quantitative nodal attribute |

nodeifactor(attr, base=1, levels=-1)
The optional
For an analogous term for quantitative vertex attributes, see | binary, dyad-independent, directed, categorical nodal attribute, frequently-used |

nodeifactor(attr, base=1, levels=-1, form=“sum”)
The optional
For an analogous term for quantitative vertex attributes, see | valued, dyad-independent, directed, categorical nodal attribute |

nodeisqrtcovar()
| valued, directed, non-negative, quantitative nodal attribute |

nodematch(attr, diff=FALSE, keep=NULL, levels=NULL)
By default, matches on all levels
| binary, dyad-independent, frequently-used, directed, undirected, categorical nodal attribute |

nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form=“sum”)
By default, matches on all levels
| valued, dyad-independent, directed, undirected, categorical nodal attribute |

match()
By default, matches on all levels
| binary, directed, dyad-independent, undirected, categorical nodal attribute |

nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=NULL)
The argument | binary, dyad-independent, frequently-used, directed, undirected, categorical nodal attribute |

nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=NULL, form=“sum”)
The argument | valued, dyad-independent, directed, undirected, categorical nodal attribute |

nodeocov(attr)
| binary, directed, dyad-independent, quantitative nodal attribute |

nodeocov(attr, form=“sum”)
| valued, directed, dyad-independent |

nodeocovar()
| valued, directed, quantitative nodal attribute |

nodeofactor(attr, base=1, levels=-1)
The optional
This term can only be used with directed networks. | binary, dyad-independent, directed, categorical nodal attribute |

nodeofactor(attr, base=1, levels=-1, form=“sum”)
The optional
This term can only be used with directed networks. | valued, dyad-independent, categorical nodal attribute, directed |

nodeosqrtcovar()
| valued, directed, non-negative |

nodesqrtcovar(center=TRUE)
| valued, non-negative, directed, undirected, quantitative nodal attribute |

nsp(d)
| binary, directed, undirected |

odegrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL)
This term can only be used with directed networks; for undirected networks (bipartite and not) see | binary, directed, categorical nodal attribute |

odegree(d, by=NULL, homophily=FALSE, levels=NULL)
| binary, directed, categorical nodal attribute, frequently-used |

odegree1.5()
| binary, directed |

odegreepopularity()
| binary, directed, deprecated |

opentriad()
| binary, undirected, triad-related |

ostar(k, attr=NULL, levels=NULL)
| binary, directed, categorical nodal attribute |

receiver(base=1, nodes=-1)
| binary, directed, dyad-independent |

receiver(base=1, nodes=-1, form=“sum”)
| valued, directed, dyad-independent |

sender(base=1, nodes=-1)
The argument This term can only be used with directed networks. For undirected networks, see | binary, directed, dyad-independent |

sender(base=1, nodes=-1, form=“sum”)
The argument This term can only be used with directed networks. For undirected networks, see | valued, directed, dyad-independent |

simmelian()
| binary, directed, triad-related |

simmelianties()
| binary, triad-related, directed |

smalldiff(attr, cutoff)
| binary, dyad-independent, directed, undirected, quantitative nodal attribute |

sociality(attr=NULL, base=1, levels=NULL, nodes=-1)
The argument | binary, undirected, dyad-independent, categorical nodal attribute |

sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form=“sum”)
| valued, undirected, dyad-independent, categorical nodal attribute |

sum(pow=1)
| valued, directed, undirected |

threetrail(keep=NULL, levels=NULL)
The argument | binary, directed, undirected |

transitive()
| binary, directed, triad-related |

transitiveties(attr=NULL, levels=NULL)
| binary, directed, triad-related, categorical nodal attribute |

transitiveties(threshold=0)
| valued, directed, undirected, triad-related |

transitiveweights(twopath=“min”,combine=“max”,affect=“min”)
| valued, directed, undirected, non-negative, triad-related |

triadcensus(levels)
| binary, triad-related, directed, undirected |

triangle(attr=NULL, diff=FALSE, levels=NULL)
| binary, frequently-used, triad-related, directed, undirected, categorical nodal attribute |

tripercent(attr=NULL, diff=FALSE, levels=NULL)
| binary, undirected, triad-related, categorical nodal attribute |

ttriple(attr=NULL, diff=FALSE, levels=NULL)
| binary, directed, triad-related, categorical nodal attribute |

ttriad()
| binary, directed, triad-related, categorical nodal attribute |

twopath()
| binary, directed, undirected |

Note that currently the categories are somewhat ambiguous in their exclusivity. For example, a term marked as 'directed' can not be used with an undirected network, but a term not marked with either 'directed' or 'undirected' can be used with both. (rename to 'directed-only' ?)

Jump to category:binary dyad-independent frequently-used directed undirected quantitative nodal attribute valued categorical nodal attribute curved triad-related bipartite quantitative nodalattribute deprecated non-negative

- absdiff : Absolute difference
- absdiffcat : Categorical absolute difference
- altkstar : Alternating k-star
- asymmetric : Asymmetric dyads
- b1concurrent : Concurrent node count for the first mode in a bipartite (aka two-mode) network
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1degrange : Degree range for the first mode in a bipartite (a.k.a. two-mode) network
- b1degree : Degree for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1mindegree : Minimum degree for the first mode in a bipartite (aka two-mode) network
- b1nodematch : Nodal attribute-based homophily effect for the first mode in a bipartite (aka two-mode) network
- b1sociality : Degree
- b1star : k-Stars for the first mode in a bipartite (aka two-mode) network
- b1starmix : Mixing matrix for k-stars centered on the first mode of a bipartite network
- b1twostar : Two-star census for central nodes centered on the first mode of a bipartite network
- b2concurrent : Concurrent node count for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2degrange : Degree range for the second mode in a bipartite (a.k.a. two-mode) network
- b2degree : Degree for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2mindegree : Minimum degree for the second mode in a bipartite (aka two-mode) network
- b2nodematch : Nodal attribute-based homophily effect for the second mode in a bipartite (aka two-mode) network
- b2sociality : Degree
- b2star : k-Stars for the second mode in a bipartite (aka two-mode) network
- b2starmix : Mixing matrix for k-stars centered on the second mode of a bipartite network
- b2twostar : Two-star census for central nodes centered on the second mode of a bipartite network
- balance : Balanced triads
- coincidence : Coincident node count for the second mode in a bipartite (aka two-mode) network
- concurrent : Concurrent node count
- concurrentties : Concurrent tie count
- ctriple : Cyclic triples
- ctriad : Cyclic triples
- cycle : Cycles
- cyclicalties : Cyclical ties
- ddsp :
- degrange : Degree range
- degree : Degree
- degree1.5 : Degree to the 3/2 power
- degreepopularity : Degree popularity (deprecated)
- degcrossprod : Degree Cross-Product
- degcor : Degree Correlation
- density : Density
- diff : Difference
- desp :
- dgwdsp :
- dgwesp :
- dgwnsp :
- dnsp :
- dsp : Dyadwise shared partners
- dyadcov : Dyadic covariate
- edgecov : Edge covariate
- edges : Edges
- esp : Edgewise shared partners
- gwb1degree : Geometrically weighted degree distribution for the first mode in a bipartite (aka two-mode) network
- gwb2degree : Geometrically weighted degree distribution for the second mode in a bipartite (aka two-mode) network
- gwdegree : Geometrically weighted degree distribution
- gwdsp : Geometrically weighted dyadwise shared partner distribution
- gwesp : Geometrically weighted edgewise shared partner distribution
- gwidegree : Geometrically weighted in-degree distribution
- gwnsp : Geometrically weighted nonedgewise shared partner distribution
- gwodegree : Geometrically weighted out-degree distribution
- hamming : Hamming distance
- hammingmix : Hamming distance within mixing
- idegrange : In-degree range
- idegree : In-degree
- idegree1.5 : In-degree to the 3/2 power
- idegreepopularity : In-degree popularity (deprecated)
- intransitive : Intransitive triads
- isolates : Isolates
- istar : In-stars
- kstar : k-Stars
- localtriangle : Triangles within neighborhoods
- m2star : Mixed 2-stars, a.k.a 2-paths
- meandeg : Mean vertex degree
- mm : Mixing matrix cells and margins
- mutual : Mutuality
- nearsimmelian : Near simmelian triads
- nodecov : Main effect of a covariate
- nodemain : Main effect of a covariate
- nodefactor : Factor attribute effect
- nodeicov : Main effect of a covariate for in-edges
- nodeifactor : Factor attribute effect for in-edges
- nodematch : Uniform homophily and differential homophily
- match : Uniform homophily and differential homophily
- nodemix : Nodal attribute mixing
- nodeocov : Main effect of a covariate for out-edges
- nodeofactor : Factor attribute effect for out-edges
- nsp : Nonedgewise shared partners
- odegrange : Out-degree range
- odegree : Out-degree
- odegree1.5 : Out-degree to the 3/2 power
- odegreepopularity : Out-degree popularity (deprecated)
- opentriad : Open triads
- ostar : k-Outstars
- receiver : Receiver effect
- sender : Sender effect
- simmelian : Simmelian triads
- simmelianties : Ties in simmelian triads
- smalldiff : Number of ties between actors with similar (but not necessarily identical) attribute values
- sociality : Undirected degree
- threetrail : Three-trails
- transitive : Transitive triads
- transitiveties : Transitive ties
- triadcensus : Triad census
- triangle : Triangles
- tripercent : Triangle percentage
- ttriple : Transitive triples
- ttriad : Transitive triples
- twopath : 2-Paths

- absdiff : Absolute difference
- absdiff : Absolute difference
- absdiffcat : Categorical absolute difference
- absdiffcat : Categorical absolute difference
- asymmetric : Asymmetric dyads
- atleast : Number of dyads with values greater than or equal to a threshol
- atmost : Number of dyads with values less than or equal to a threshol
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1nodematch : Nodal attribute-based homophily effect for the first mode in a bipartite (aka two-mode) network
- b1sociality : Degree
- b1sociality : Degree
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2nodematch : Nodal attribute-based homophily effect for the second mode in a bipartite (aka two-mode) network
- b2sociality : Degree
- b2sociality : Degree
- density : Density
- diff : Difference
- diff : Difference
- dyadcov : Dyadic covariate
- edgecov : Edge covariate
- edgecov : Edge covariate
- edges : Edges
- nonzero : Edges
- equalto : Number of dyads with values equal to a specific value (within tolerance)
- greaterthan : Number of dyads with values strictly greater than a threshold
- hamming : Hamming distance
- hammingmix : Hamming distance within mixing
- ininterval : Number of dyads whose values are in an interva
- smallerthan : Number of dyads with values strictly smaller than a threshold
- meandeg : Mean vertex degree
- mm : Mixing matrix cells and margins
- mm : Mixing matrix cells and margins
- nodecov : Main effect of a covariate
- nodecov : Main effect of a covariate
- nodefactor : Factor attribute effect
- nodefactor : Factor attribute effect
- nodeifactor : Factor attribute effect for in-edges
- nodeifactor : Factor attribute effect for in-edges
- nodematch : Uniform homophily and differential homophily
- nodematch : Uniform homophily and differential homophily
- match : Uniform homophily and differential homophily
- nodemix : Nodal attribute mixing
- nodemix : Nodal attribute mixing
- nodeocov : Main effect of a covariate for out-edges
- nodeocov : Main effect of a covariate for out-edges
- nodeofactor : Factor attribute effect for out-edges
- nodeofactor : Factor attribute effect for out-edges
- receiver : Receiver effect
- receiver : Receiver effect
- sender : Sender effect
- sender : Sender effect
- smalldiff : Number of ties between actors with similar (but not necessarily identical) attribute values
- sociality : Undirected degree
- sociality : Undirected degree

- absdiff : Absolute difference
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1degree : Degree for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1nodematch : Nodal attribute-based homophily effect for the first mode in a bipartite (aka two-mode) network
- b2concurrent : Concurrent node count for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2degree : Degree for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2nodematch : Nodal attribute-based homophily effect for the second mode in a bipartite (aka two-mode) network
- degree : Degree
- diff : Difference
- edgecov : Edge covariate
- edges : Edges
- gwdegree : Geometrically weighted degree distribution
- gwesp : Geometrically weighted edgewise shared partner distribution
- idegree : In-degree
- isolates : Isolates
- mm : Mixing matrix cells and margins
- mm : Mixing matrix cells and margins
- mutual : Mutuality
- nodecov : Main effect of a covariate
- nodefactor : Factor attribute effect
- nodeicov : Main effect of a covariate for in-edges
- nodeifactor : Factor attribute effect for in-edges
- nodematch : Uniform homophily and differential homophily
- nodemix : Nodal attribute mixing
- odegree : Out-degree
- triangle : Triangles

- absdiff : Absolute difference
- absdiff : Absolute difference
- absdiffcat : Categorical absolute difference
- absdiffcat : Categorical absolute difference
- asymmetric : Asymmetric dyads
- atleast : Number of dyads with values greater than or equal to a threshol
- atmost : Number of dyads with values less than or equal to a threshol
- balance : Balanced triads
- ctriple : Cyclic triples
- ctriad : Cyclic triples
- cycle : Cycles
- cyclicalties : Cyclical ties
- cyclicalties : Cyclical ties
- cyclicalweights : Cyclical weights
- ddsp :
- density : Density
- diff : Difference
- diff : Difference
- desp :
- dgwdsp :
- dgwesp :
- dgwnsp :
- dnsp :
- dsp : Dyadwise shared partners
- dyadcov : Dyadic covariate
- edgecov : Edge covariate
- edgecov : Edge covariate
- edges : Edges
- nonzero : Edges
- esp : Edgewise shared partners
- equalto : Number of dyads with values equal to a specific value (within tolerance)
- greaterthan : Number of dyads with values strictly greater than a threshold
- gwdsp : Geometrically weighted dyadwise shared partner distribution
- gwesp : Geometrically weighted edgewise shared partner distribution
- gwidegree : Geometrically weighted in-degree distribution
- gwnsp : Geometrically weighted nonedgewise shared partner distribution
- gwodegree : Geometrically weighted out-degree distribution
- hamming : Hamming distance
- hammingmix : Hamming distance within mixing
- idegrange : In-degree range
- idegree : In-degree
- idegree1.5 : In-degree to the 3/2 power
- idegreepopularity : In-degree popularity (deprecated)
- ininterval : Number of dyads whose values are in an interva
- intransitive : Intransitive triads
- isolates : Isolates
- istar : In-stars
- smallerthan : Number of dyads with values strictly smaller than a threshold
- localtriangle : Triangles within neighborhoods
- m2star : Mixed 2-stars, a.k.a 2-paths
- meandeg : Mean vertex degree
- mm : Mixing matrix cells and margins
- mm : Mixing matrix cells and margins
- mutual : Mutuality
- mutual : Mutuality
- nearsimmelian : Near simmelian triads
- nodecov : Main effect of a covariate
- nodecov : Main effect of a covariate
- nodemain : Main effect of a covariate
- nodecovar : Uncentered covariance of dyad values incident on each actor
- nodefactor : Factor attribute effect
- nodefactor : Factor attribute effect
- nodeicov : Main effect of a covariate for in-edges
- nodeicov : Main effect of a covariate for in-edges
- nodeicovar : Uncentered covariance of in-dyad values incident on each actor
- nodeifactor : Factor attribute effect for in-edges
- nodeifactor : Factor attribute effect for in-edges
- nodeisqrtcovar : Uncentered covariance of square roots of in-dyad values incident on each actor
- nodematch : Uniform homophily and differential homophily
- nodematch : Uniform homophily and differential homophily
- match : Uniform homophily and differential homophily
- nodemix : Nodal attribute mixing
- nodemix : Nodal attribute mixing
- nodeocov : Main effect of a covariate for out-edges
- nodeocov : Main effect of a covariate for out-edges
- nodeocovar : Uncentered covariance of out-dyad values incident on each actor
- nodeofactor : Factor attribute effect for out-edges
- nodeofactor : Factor attribute effect for out-edges
- nodeosqrtcovar : Uncentered covariance of square roots of out-dyad values incident on each actor
- nodesqrtcovar : Covariance of square roots of dyad values incident on each actor
- nsp : Nonedgewise shared partners
- odegrange : Out-degree range
- odegree : Out-degree
- odegree1.5 : Out-degree to the 3/2 power
- odegreepopularity : Out-degree popularity (deprecated)
- ostar : k-Outstars
- receiver : Receiver effect
- receiver : Receiver effect
- sender : Sender effect
- sender : Sender effect
- simmelian : Simmelian triads
- simmelianties : Ties in simmelian triads
- smalldiff : Number of ties between actors with similar (but not necessarily identical) attribute values
- sum : Sum of dyad values (optionally taken to a power)
- threetrail : Three-trails
- transitive : Transitive triads
- transitiveties : Transitive ties
- transitiveties : Transitive ties
- transitiveweights : Transitive weights
- triadcensus : Triad census
- triangle : Triangles
- ttriple : Transitive triples
- ttriad : Transitive triples
- twopath : 2-Paths

- absdiff : Absolute difference
- absdiff : Absolute difference
- absdiffcat : Categorical absolute difference
- absdiffcat : Categorical absolute difference
- altkstar : Alternating k-star
- atleast : Number of dyads with values greater than or equal to a threshol
- atmost : Number of dyads with values less than or equal to a threshol
- b1concurrent : Concurrent node count for the first mode in a bipartite (aka two-mode) network
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1degrange : Degree range for the first mode in a bipartite (a.k.a. two-mode) network
- b1degree : Degree for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1mindegree : Minimum degree for the first mode in a bipartite (aka two-mode) network
- b1sociality : Degree
- b1sociality : Degree
- b1star : k-Stars for the first mode in a bipartite (aka two-mode) network
- b1starmix : Mixing matrix for k-stars centered on the first mode of a bipartite network
- b1twostar : Two-star census for central nodes centered on the first mode of a bipartite network
- b2concurrent : Concurrent node count for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2degrange : Degree range for the second mode in a bipartite (a.k.a. two-mode) network
- b2degree : Degree for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2mindegree : Minimum degree for the second mode in a bipartite (aka two-mode) network
- b2sociality : Degree
- b2sociality : Degree
- b2star : k-Stars for the second mode in a bipartite (aka two-mode) network
- b2starmix : Mixing matrix for k-stars centered on the second mode of a bipartite network
- b2twostar : Two-star census for central nodes centered on the second mode of a bipartite network
- balance : Balanced triads
- coincidence : Coincident node count for the second mode in a bipartite (aka two-mode) network
- concurrent : Concurrent node count
- concurrentties : Concurrent tie count
- cycle : Cycles
- cyclicalties : Cyclical ties
- cyclicalweights : Cyclical weights
- degrange : Degree range
- degree : Degree
- degree1.5 : Degree to the 3/2 power
- degreepopularity : Degree popularity (deprecated)
- degcrossprod : Degree Cross-Product
- degcor : Degree Correlation
- density : Density
- diff : Difference
- diff : Difference
- dsp : Dyadwise shared partners
- dyadcov : Dyadic covariate
- edgecov : Edge covariate
- edgecov : Edge covariate
- edges : Edges
- nonzero : Edges
- esp : Edgewise shared partners
- equalto : Number of dyads with values equal to a specific value (within tolerance)
- greaterthan : Number of dyads with values strictly greater than a threshold
- gwb1degree : Geometrically weighted degree distribution for the first mode in a bipartite (aka two-mode) network
- gwb2degree : Geometrically weighted degree distribution for the second mode in a bipartite (aka two-mode) network
- gwdegree : Geometrically weighted degree distribution
- gwdsp : Geometrically weighted dyadwise shared partner distribution
- gwesp : Geometrically weighted edgewise shared partner distribution
- gwnsp : Geometrically weighted nonedgewise shared partner distribution
- hamming : Hamming distance
- ininterval : Number of dyads whose values are in an interva
- isolates : Isolates
- kstar : k-Stars
- smallerthan : Number of dyads with values strictly smaller than a threshold
- localtriangle : Triangles within neighborhoods
- meandeg : Mean vertex degree
- mm : Mixing matrix cells and margins
- mm : Mixing matrix cells and margins
- nodecov : Main effect of a covariate
- nodecov : Main effect of a covariate
- nodemain : Main effect of a covariate
- nodecovar : Uncentered covariance of dyad values incident on each actor
- nodefactor : Factor attribute effect
- nodefactor : Factor attribute effect
- nodematch : Uniform homophily and differential homophily
- nodematch : Uniform homophily and differential homophily
- match : Uniform homophily and differential homophily
- nodemix : Nodal attribute mixing
- nodemix : Nodal attribute mixing
- nodesqrtcovar : Covariance of square roots of dyad values incident on each actor
- nsp : Nonedgewise shared partners
- opentriad : Open triads
- sociality : Undirected degree
- sociality : Undirected degree
- sum : Sum of dyad values (optionally taken to a power)
- threetrail : Three-trails
- transitiveties : Transitive ties
- transitiveweights : Transitive weights
- triadcensus : Triad census
- triangle : Triangles
- tripercent : Triangle percentage
- twopath : 2-Paths

- absdiff : Absolute difference
- absdiff : Absolute difference
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- diff : Difference
- diff : Difference
- nodecov : Main effect of a covariate
- nodecov : Main effect of a covariate
- nodecovar : Uncentered covariance of dyad values incident on each actor
- nodeicov : Main effect of a covariate for in-edges
- nodeicov : Main effect of a covariate for in-edges
- nodeicovar : Uncentered covariance of in-dyad values incident on each actor
- nodeisqrtcovar : Uncentered covariance of square roots of in-dyad values incident on each actor
- nodeocov : Main effect of a covariate for out-edges
- nodeocovar : Uncentered covariance of out-dyad values incident on each actor
- nodesqrtcovar : Covariance of square roots of dyad values incident on each actor

- absdiff : Absolute difference
- absdiffcat : Categorical absolute difference
- atleast : Number of dyads with values greater than or equal to a threshol
- atmost : Number of dyads with values less than or equal to a threshol
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1sociality : Degree
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2sociality : Degree
- cyclicalties : Cyclical ties
- cyclicalweights : Cyclical weights
- diff : Difference
- edgecov : Edge covariate
- edges : Edges
- nonzero : Edges
- equalto : Number of dyads with values equal to a specific value (within tolerance)
- greaterthan : Number of dyads with values strictly greater than a threshold
- ininterval : Number of dyads whose values are in an interva
- smallerthan : Number of dyads with values strictly smaller than a threshold
- mm : Mixing matrix cells and margins
- mutual : Mutuality
- nodecov : Main effect of a covariate
- nodecovar : Uncentered covariance of dyad values incident on each actor
- nodefactor : Factor attribute effect
- nodeicov : Main effect of a covariate for in-edges
- nodeicovar : Uncentered covariance of in-dyad values incident on each actor
- nodeifactor : Factor attribute effect for in-edges
- nodeisqrtcovar : Uncentered covariance of square roots of in-dyad values incident on each actor
- nodematch : Uniform homophily and differential homophily
- nodemix : Nodal attribute mixing
- nodeocov : Main effect of a covariate for out-edges
- nodeocovar : Uncentered covariance of out-dyad values incident on each actor
- nodeofactor : Factor attribute effect for out-edges
- nodeosqrtcovar : Uncentered covariance of square roots of out-dyad values incident on each actor
- nodesqrtcovar : Covariance of square roots of dyad values incident on each actor
- receiver : Receiver effect
- sender : Sender effect
- sociality : Undirected degree
- sum : Sum of dyad values (optionally taken to a power)
- transitiveties : Transitive ties
- transitiveweights : Transitive weights

- absdiffcat : Categorical absolute difference
- absdiffcat : Categorical absolute difference
- altkstar : Alternating k-star
- b1concurrent : Concurrent node count for the first mode in a bipartite (aka two-mode) network
- b1degree : Degree for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1star : k-Stars for the first mode in a bipartite (aka two-mode) network
- b1starmix : Mixing matrix for k-stars centered on the first mode of a bipartite network
- b1twostar : Two-star census for central nodes centered on the first mode of a bipartite network
- b2degree : Degree for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2star : k-Stars for the second mode in a bipartite (aka two-mode) network
- b2starmix : Mixing matrix for k-stars centered on the second mode of a bipartite network
- b2twostar : Two-star census for central nodes centered on the second mode of a bipartite network
- concurrent : Concurrent node count
- concurrentties : Concurrent tie count
- ctriple : Cyclic triples
- ctriad : Cyclic triples
- degrange : Degree range
- degree : Degree
- dyadcov : Dyadic covariate
- idegrange : In-degree range
- idegree : In-degree
- istar : In-stars
- kstar : k-Stars
- mm : Mixing matrix cells and margins
- mm : Mixing matrix cells and margins
- nodefactor : Factor attribute effect
- nodefactor : Factor attribute effect
- nodeifactor : Factor attribute effect for in-edges
- nodeifactor : Factor attribute effect for in-edges
- nodematch : Uniform homophily and differential homophily
- nodematch : Uniform homophily and differential homophily
- match : Uniform homophily and differential homophily
- nodemix : Nodal attribute mixing
- nodemix : Nodal attribute mixing
- nodeofactor : Factor attribute effect for out-edges
- nodeofactor : Factor attribute effect for out-edges
- odegrange : Out-degree range
- odegree : Out-degree
- ostar : k-Outstars
- sociality : Undirected degree
- sociality : Undirected degree
- transitiveties : Transitive ties
- triangle : Triangles
- tripercent : Triangle percentage
- ttriple : Transitive triples
- ttriad : Transitive triples

- altkstar : Alternating k-star
- gwb1degree : Geometrically weighted degree distribution for the first mode in a bipartite (aka two-mode) network
- gwb2degree : Geometrically weighted degree distribution for the second mode in a bipartite (aka two-mode) network
- gwdegree : Geometrically weighted degree distribution
- gwdsp : Geometrically weighted dyadwise shared partner distribution
- gwesp : Geometrically weighted edgewise shared partner distribution
- gwidegree : Geometrically weighted in-degree distribution
- gwnsp : Geometrically weighted nonedgewise shared partner distribution
- gwodegree : Geometrically weighted out-degree distribution

- asymmetric : Asymmetric dyads
- balance : Balanced triads
- ctriple : Cyclic triples
- ctriad : Cyclic triples
- intransitive : Intransitive triads
- localtriangle : Triangles within neighborhoods
- nearsimmelian : Near simmelian triads
- opentriad : Open triads
- simmelian : Simmelian triads
- simmelianties : Ties in simmelian triads
- transitive : Transitive triads
- transitiveties : Transitive ties
- transitiveties : Transitive ties
- transitiveweights : Transitive weights
- triadcensus : Triad census
- triangle : Triangles
- tripercent : Triangle percentage
- ttriple : Transitive triples
- ttriad : Transitive triples

- b1concurrent : Concurrent node count for the first mode in a bipartite (aka two-mode) network
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network
- b1degrange : Degree range for the first mode in a bipartite (a.k.a. two-mode) network
- b1degree : Degree for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1factor : Factor attribute effect for the first mode in a bipartite (aka two-mode) network
- b1mindegree : Minimum degree for the first mode in a bipartite (aka two-mode) network
- b1sociality : Degree
- b1sociality : Degree
- b1star : k-Stars for the first mode in a bipartite (aka two-mode) network
- b1starmix : Mixing matrix for k-stars centered on the first mode of a bipartite network
- b1twostar : Two-star census for central nodes centered on the first mode of a bipartite network
- b2concurrent : Concurrent node count for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2cov : Main effect of a covariate for the second mode in a bipartite (aka two-mode) network
- b2degrange : Degree range for the second mode in a bipartite (a.k.a. two-mode) network
- b2degree : Degree for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2factor : Factor attribute effect for the second mode in a bipartite (aka two-mode) network
- b2mindegree : Minimum degree for the second mode in a bipartite (aka two-mode) network
- b2sociality : Degree
- b2sociality : Degree
- b2star : k-Stars for the second mode in a bipartite (aka two-mode) network
- b2starmix : Mixing matrix for k-stars centered on the second mode of a bipartite network
- b2twostar : Two-star census for central nodes centered on the second mode of a bipartite network
- coincidence : Coincident node count for the second mode in a bipartite (aka two-mode) network
- diff : Difference

- b1cov : Main effect of a covariate for the first mode in a bipartite (aka two-mode) network

- degreepopularity : Degree popularity (deprecated)
- idegreepopularity : In-degree popularity (deprecated)
- odegreepopularity : Out-degree popularity (deprecated)

- nodeisqrtcovar : Uncentered covariance of square roots of in-dyad values incident on each actor
- nodeosqrtcovar : Uncentered covariance of square roots of out-dyad values incident on each actor
- nodesqrtcovar : Covariance of square roots of dyad values incident on each actor
- transitiveweights : Transitive weights

This documentation was built with..

```
sessionInfo()
```

```
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)
##
## Matrix products: default
## BLAS: /home/levap/R-3.6.0/lib/libRblas.so
## LAPACK: /home/levap/R-3.6.0/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.utf8 LC_COLLATE=C
## [5] LC_MONETARY=en_AU.utf8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_AU.utf8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.utf8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ergm_3.10.4 network_1.15
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 knitr_1.23 magrittr_1.5
## [4] MASS_7.3-51.4 tidyselect_0.2.5 lattice_0.20-38
## [7] R6_2.4.0 rlang_0.3.4 stringr_1.4.0
## [10] dplyr_0.8.1 tools_3.6.0 parallel_3.6.0
## [13] grid_3.6.0 lpSolve_5.6.13.1 xfun_0.7
## [16] coda_0.19-2 htmltools_0.3.6 digest_0.6.19
## [19] assertthat_0.2.1 tibble_2.1.3 crayon_1.3.4
## [22] Matrix_1.2-17 purrr_0.3.2 trust_0.1-7
## [25] robustbase_0.93-5 glue_1.3.1 evaluate_0.14
## [28] rmarkdown_1.13 statnet.common_4.3.0 stringi_1.4.3
## [31] DEoptimR_1.0-8 compiler_3.6.0 pillar_1.4.1
## [34] pkgconfig_2.0.2
```