Learn R Programming

ergm.sign (version 0.1.2)

Exponential-Family Models for Signed Networks

Description

Extends the 'ergm.multi' packages from the Statnet suite to fit (temporal) exponential-family random graph models for signed networks. The framework models positive and negative ties as interdependent, which allows estimation and testing of structural balance theory. The package also includes options for descriptive summaries, visualization, and simulation of signed networks. See Krivitsky, Koehly, and Marcum (2020) and Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2025) .

Copy Link

Version

Install

install.packages('ergm.sign')

Monthly Downloads

144

Version

0.1.2

License

MIT + file LICENSE

Maintainer

Marc Schalberger

Last Published

February 3rd, 2026

Functions in ergm.sign (0.1.2)

gwdse-ergmTerm

Geometrically weighted dyadwise shared enemies distribution
networks.sign

Combine Signed Networks into a Multi- or Dynamic-Network Object
nse-ergmTerm

Non-edgewise shared enemies
snctrl

Statnet Control
sponsor

Common Sponsor Data for Syrian Civil War Factions
plot.dynamic.sign

Visualization for Dynamic Signed Networks
tribes

Read Highland Tribes
summary.static.sign

Network Attributes for Signed Networks
rebels_pooled

Conflict Events in Syrian Civil War
summary_formula.dynamic.sign

Summary formula method for dynamic signed networks
rebels

Conflict Events in Syrian Civil War
gwdsf-ergmTerm

Geometrically weighted dyadwise shared friends distribution
network.sign

Create Signed Network Object
fixL-ergmConstraint

Logical layer constraint
eval_loglik

Evaluate Log-Likelihood via Path Sampling
mple_sign

Fit an ERGM with MPLE using a logistic regression model
InitErgmTerm.delrecip

Delayed reciprocity
GoF

Conduct Goodness-of-Fit Diagnostics for a Signed ERGM
InitErgmTerm.delnodematch

Delayed node matching on attribute (lag-1)
dse-ergmTerm

Dyadwise shared enemies
dsf-ergmTerm

Dyadwise shared friends
InitErgmTerm.Neg

Evaluation of negative edges
ergm.sign

ergm.sign: A Package for Exponential Random Graph Models for Signed Networks
UnLayer

Multilayer network to single layer network.
TNTFixL-ergmProposal

Default MH algorithm respecting the layer constraint
InitErgmTerm.Pos

Evaluation of positive edges
gwesf-ergmTerm

Geometrically weighted edgewise shared friend distribution
gwese-ergmTerm

Geometrically weighted edgewise shared enemy distribution
ese-ergmTerm

Edgewise shared enemies
nsf-ergmTerm

Non-edgewise shared friends
gwnse-ergmTerm

Geometrically weighted non-edgewise shared enemey distribution
esf-ergmTerm

Edgewise shared friends
randomtoggleFixL-ergmProposal

Propose a randomly selected dyad to toggle, respecting the layer constraint
plot.static.sign

Visualization for Signed Networks
gwnsf-ergmTerm

Geometrically weighted non-edgewise shared friend distribution