Help for package ergm.sign

Type:

Package

Title:

Exponential-Family Models for Signed Networks

Version:

0.1.3

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) <doi:10.1007/s11336-020-09720-7> and Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2025) <doi:10.1017/pan.2024.21>.

Encoding:

UTF-8

LazyData:

true

LazyLoad:

yes

RoxygenNote:

7.3.3

RdMacros:

Rdpack

Imports:

statnet.common, network, ergm, ergm.multi, dplyr, magrittr, tergm, Rdpack, intergraph, graphlayouts, igraph, purrr, graphics, methods, utils

LinkingTo:

ergm.multi, ergm

Suggests:

knitr, rmarkdown, testthat

VignetteBuilder:

knitr

Depends:

R (≥ 3.5.0)

License:

MIT + file LICENSE

NeedsCompilation:

yes

Packaged:

2026-07-20 15:25:01 UTC; mschalberger

Author:

Marc Schalberger [cre], Cornelius Fritz [aut], Pavel Krivitsky [ctb]

Maintainer:

Marc Schalberger <m.schalberger@fu-berlin.de>

Repository:

CRAN

Date/Publication:

2026-07-20 19:40:02 UTC

At least one common enemy

Description

This term adds one network statistic to the model equal to the number of edges in the network with at least one shared enemy. For a directed network, multiple shared enemy definitions are possible.

Usage

#binary: CE(type="OTP")

Arguments

type

A string indicating the type of shared partner or path to be considered for directed networks: '"OTP"' (default for directed), '"ITP"', '"RTP"', '"OSP"', and '"ISP"'; has no effect for undirected. See the section below on Shared partner types for details.

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to 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 \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

At leat one common friend

Description

This term adds one network statistic to the model equal to the number of edges in the network with at least one shared friend. For a directed network, multiple shared friend definitions are possible.

Usage

#binary: CF(type="OTP")

Arguments

type

Shared partner types

Note

Evaluation of negative edges

Description

Evaluates the terms in 'formula' of the negative edges and sums the results elementwise.

Usage

# binary: Neg(formula)

Arguments

formula

a one-sided [ergm()]-style formula with the terms to be evaluated

Evaluation of positive edges

Description

Evaluates the terms in 'formula' of the positive edges and sums the results elementwise.

Usage

# binary: Pos(formula)

Arguments

formula

a one-sided [ergm()]-style formula with the terms to be evaluated

Delayed node matching on attribute (lag-1)

Description

Create a nodematch term where node attributes come from the previous network's node attribute 'attr'. The previous network used is the one indexed by the current 'GroupID' (equivalent to 'lag=1' previously). This constructs a temporary node attribute 'delnodecov_<attr>' on the current network (copied from the previous net) and calls 'nodematch' on that attribute.

Usage

# binary: delnodematch(attr)

Arguments

attr

character attribute name to copy from the previous network into the current.

Delayed reciprocity

Description

For the current network layer 'base', this term equals 1 for each directed edge i->j currently present where the reverse edge j->i was present in the previous network's same layer. The previous network used is the one indexed by the current 'GroupID' (i.e., the behaviour is the same as the prior 'lag=1' implementation). The term is provided as an edgecov (1/0).

Usage

# binary: delrecip(base)

Arguments

base

character or numeric name/identifier of the layer to examine in the current network.

Construct a Signed Network Representation for ERGM

Description

'Signed()' is a convenience wrapper around [Layer()] that constructs a two-layer multilayer network representation of a signed network, with one layer for positive ties ('pos') and one for negative ties ('neg'). It accepts several input formats and handles the layer construction and constraint specification automatically.

Usage

Signed(
  ...,
  dual.sign = FALSE,
  .symmetric = NULL,
  .bipartite = NULL,
  .active = NULL
)

Arguments

...

Input networks or edge attributes. Three call signatures are supported:

'Signed(pos_nw, neg_nw)': Two separate ['network'] objects, the first containing positive ties and the second negative ties.
'Signed(nw, sign_attr)': A single ['network'] object and the name of an edge attribute (character string) whose values encode the sign of each tie. Values must be in {-1, 0, 1}, where '1' indicates a positive tie, '-1' a negative tie, and '0' (or 'NA') an unsigned or absent tie.
'Signed(nw, pos_attr, neg_attr)': A single ['network'] object and the names of two separate binary edge attributes encoding positive and negative ties respectively.

dual.sign

Logical. If 'FALSE' (the default), a constraint is added to prevent any dyad from simultaneously having both a positive and a negative tie. Set to 'TRUE' to allow dual-signed ties, e.g. in multiplex contexts where the same dyad may appear in both layers.

.symmetric

Passed to [Layer()]. A logical vector indicating which layers should be treated as undirected (symmetrized) even if the underlying network is directed.

.bipartite

Passed to [Layer()]. An integer vector indicating the bipartite block size for each layer.

.active

Passed to [Layer()]. A list of vertex attribute specifications, one per layer, indicating which vertices are active in each layer.

Value

A combined multilayer ['network'] object as returned by [Layer()], with two layers named '"pos"' and '"neg"'.

Examples

## Not run: 
# Two separate network objects
fit <- ergm.sign(Signed(pos_nw, neg_nw) ~ Pos(~edges) + Neg(~edges))

# Single network with a {-1, 0, 1}-valued edge attribute
fit <- ergm.sign(Signed(nw, "sign") ~ Pos(~edges) + Neg(~edges))

# Single network with two separate binary edge attributes
fit <- ergm.sign(Signed(nw, "is_pos", "is_neg") ~ Pos(~edges) + Neg(~edges))

# Allow dual-signed ties (pos and neg simultaneously)
fit <- ergm.sign(Signed(nw, "sign", dual.sign = TRUE) ~ Pos(~edges) + Neg(~edges))

## End(Not run)

Multilayer network to single layer network.

Description

Turn a multilayer network object into a single layer network object.

Usage

UnLayer(
  net,
  color_pos = "#008000",
  color_neg = "#E3000F",
  color_both = "#333333",
  neg.lty = 2,
  both.lty = 2
)

Arguments

net

A signed network object of class static.sign or dynamic.sign.

color_pos

Color for positive edges. Default is '#008000'.

color_neg

Color for negative edges. Default is '#E3000F'.

color_both

Color for edges that are both positive and negative. Default is '#333333'.

neg.lty

Line type for negative edges. Default is 2.

both.lty

Line type for edges that are both positive and negative. Default is 2.

Value

Single layer network object or a list of network objects for dynamic.sign.

Examples

data("tribes")
tribes_sgl <- UnLayer(tribes)

Dyadwise shared enemies

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of dyads in the network with exactly 'd[i]' shared enemies. For a directed network, multiple shared enemies definitions are possible.

Usage

# binary: dse(d, type="OTP")

Shared partner types

Note

Dyadwise shared friends

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of dyads in the network with exactly 'd[i]' shared friends. For a directed network, multiple shared friends definitions are possible.

Usage

# binary: dsf(d, type="OTP")

Shared partner types

Note

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

Description

The ergm.sign package implements tools to simulate and estimate Signed Exponential Random Graph Models and Temporal Signed Exponential Random Graph Models.

ergm for signed networks. The mple_sign function is used when estimate = "MPLE" or to compute initial values for MCMC. For other networks, behavior is identical to ergm::ergm.

Usage

ergm.sign(
  formula,
  estimate = "MLE",
  eval_lik = FALSE,
  ...,
  control = control.ergm()
)

Arguments

formula

An formula object specifying the ERGM.

estimate

Character string specifying the estimation method. Options are "MLE" (default) or "MPLE".

eval_lik

Logical indicating whether to evaluate the likelihood using path sampling.

...

Additional arguments passed to ergm.

control

A control.ergm object.

Value

An ergm object.

Author(s)

Marc Schalberger

Examples

## Not run: 
ergm.sign(signed_net ~ edges + triangles)

## End(Not run)

Edgewise shared enemies

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of edges in the network with exactly 'd[i]' shared enemies. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: ese(d, type="OTP", L.base=NULL)

Arguments

d

a vector of distinct integers

lag

logical; if TRUE, compute the lagged version of the ese statistic based on the previous time point's network

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Edgewise shared friends

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of edges in the network with exactly 'd[i]' shared friends. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: esf(d, type="OTP", L.base=NULL)

Arguments

d

a vector of distinct integers

lag

logical; if TRUE, compute the lagged version of the esf statistic based on the previous time point's network

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Evaluate Log-Likelihood via Path Sampling

Description

This function evaluates the log-likelihood of a fitted signed ergm model using path sampling.

Usage

eval_loglik(object)

Arguments

object

A fitted signed ergm model object.

Value

A logLik object containing the evaluated log-likelihood, degrees of freedom, and number of observations.

Conduct Goodness-of-Fit Diagnostics for a Signed ERGM

Description

Conduct Goodness-of-Fit Diagnostics for a Signed ERGM

Usage

## S3 method for class 'sign'
gof(object, nsim = 200, seed = NULL, ...)

Arguments

object

A fitted signed ERGM (SERGM) object of class "sign".

nsim

Integer; number of simulated networks. Defaults to 200.

seed

Optional integer seed.

...

Additional arguments passed to methods.

Value

An object of class "gof.sign". Print shows a summary; plot() produces diagnostic boxplots.

Geometrically weighted dyadwise shared enemies distribution

Description

This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared enemies distribution with decay parameter. Note that the GWDSE statistic is equal to the sum of GWNSE plus GWESE. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwdse(decay, fixed=FALSE, cutoff=30, type="OTP")

Arguments

decay

nonnegative decay parameter for the shared enemy or selected directed analogue count; required if 'fixed=TRUE' and ignored with a warning otherwise.

fixed

optional argument indicating whether the 'decay' parameter is fixed at the given value, or is to be fit as a curved exponential-family model (see Hunter and Handcock, 2006). The default is 'FALSE' , which means the scale parameter is not fixed and thus the model is a curved exponential family.

cutoff

This optional argument sets the number of underlying DSE terms to use in computing the statistics when 'fixed=FALSE', in order to reduce the computational burden. Its default value can also be controlled by the 'gw.cutoff' term option control parameter. (See '?control.ergm'.)

type

Shared partner types

Note

Geometrically weighted dyadwise shared friends distribution

Description

This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared friends distribution with decay parameter. Note that the GWDSF statistic is equal to the sum of GWNSF plus GWESF. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwdsf(decay, fixed=FALSE, cutoff=30, type="OTP")

Arguments

decay

nonnegative decay parameter for the shared friend or selected directed analogue count; required if 'fixed=TRUE' and ignored with a warning otherwise.

fixed

cutoff

This optional argument sets the number of underlying DSF terms to use in computing the statistics when 'fixed=FALSE', in order to reduce the computational burden. Its default value can also be controlled by the 'gw.cutoff' term option control parameter. (See '?control.ergm'.)

type

Shared partner types

Note

Geometrically weighted edgewise shared enemy distribution

Description

This term adds a statistic equal to the geometrically weighted edgewise (not dyadwise) shared enemy distribution with decay parameter. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: gwese(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL)

Arguments

decay

nonnegative decay parameter for the shared enemy or selected directed analogue count; required if 'fixed=TRUE' and ignored with a warning otherwise.

fixed

lag

logical; if TRUE, compute the lagged version of the gwese statistic based on the previous time point's network

cutoff

This optional argument sets the number of underlying ESE terms to use in computing the statistics when 'fixed=FALSE', in order to reduce the computational burden. Its default value can also be controlled by the 'gw.cutoff' term option control parameter. (See '?control.ergm'.)

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Geometrically weighted edgewise shared friend distribution

Description

This term adds a statistic equal to the geometrically weighted edgewise (not dyadwise) shared friend distribution with decay parameter. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwesf(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL)

Arguments

decay

nonnegative decay parameter for the shared friend or selected directed analogue count; required if 'fixed=TRUE' and ignored with a warning otherwise.

fixed

lag

logical; if TRUE, compute the lagged version of the gwesf statistic based on the previous time point's network

cutoff

This optional argument sets the number of underlying ESF terms to use in computing the statistics when 'fixed=FALSE', in order to reduce the computational burden. Its default value can also be controlled by the 'gw.cutoff' term option control parameter. (See '?control.ergm'.)

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Geometrically weighted non-edgewise shared enemey distribution

Description

This term adds a statistic equal to the geometrically weighted nonedgewise (that is, over dyads that do not have an edge) shared enemy distribution with decay parameter. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: gwnse(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL)

Arguments

decay

nonnegative decay parameter for the shared enemy or selected directed analogue count; required if 'fixed=TRUE' and ignored with a warning otherwise.

fixed

cutoff

This optional argument sets the number of underlying NSE terms to use in computing the statistics when 'fixed=FALSE', in order to reduce the computational burden. Its default value can also be controlled by the 'gw.cutoff' term option control parameter. (See '?control.ergm'.)

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Geometrically weighted non-edgewise shared friend distribution

Description

This term adds a statistic equal to the geometrically weighted nonedgewise (that is, over dyads that do not have an edge) shared friend distribution with decay parameter. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwnsf(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL)

Arguments

decay

nonnegative decay parameter for the shared friend or selected directed analogue count; required if 'fixed=TRUE' and ignored with a warning otherwise.

fixed

cutoff

This optional argument sets the number of underlying NSF terms to use in computing the statistics when 'fixed=FALSE', in order to reduce the computational burden. Its default value can also be controlled by the 'gw.cutoff' term option control parameter. (See '?control.ergm'.)

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Fit an ERGM with MPLE using a logistic regression model

Description

Returns a fitted logistic regression model used to calculate the maximum pseudolikelihood estimate (MPLE) of an exponential random graph model (ERGM).

Usage

mple_sign(
  formula,
  control = control.ergm(),
  seed = NULL,
  eval_lik = FALSE,
  ...
)

Arguments

formula

An ERGM formula with the network on the left-hand side.

control

A list of control parameters for ergmMPLE. By default, the covariance method is set to "Godambe".

seed

Optional integer to set the random seed for reproducibility when simulating networks for Godambe covariance estimation.

eval_lik

Logical indicating whether to evaluate the likelihood using path sampling.

...

Additional arguments passed to ergmMPLE.

Details

The MPLE is calculated by first computing matrices of positive and negative change statistics. These are then used to estimate the MPLE via logistic regression. Optionally, the covariance can be estimated using the Godambe method.

Value

An object of class ergm.

Examples

data(tribes)
mple_sign(tribes ~ Pos(~edges) + Neg(~edges))

Create Signed Network Object

Description

Turn adjacency matrices or edgelists into static or dynamic signed networks.

Usage

network.sign(
  mat = NULL,
  pos.mat = NULL,
  neg.mat = NULL,
  directed = FALSE,
  loops = FALSE,
  matrix.type = c("adjacency", "edgelist"),
  vertex.names = NULL,
  vertex.attr = NULL,
  dual.sign = FALSE,
  timepoints = NULL,
  tie.breaker = c("zero", "positive", "negative", "first", "last"),
  ...
)

Arguments

mat

(List of) signed adjacency matrices or edgelists. For dynamic networks, provide a list. Adjacency matrices must contain only -1, 0, or 1. Edgelists must have three columns: "From", "To", and "Sign".

pos.mat

Optional. Positive adjacency matrix or list of matrices.

neg.mat

Optional. Negative adjacency matrix or list of matrices. If provided, these are treated as two separate layers of the same network.

directed

Logical; should edges be interpreted as directed? Defaults to FALSE.

loops

Logical; should loops be allowed? Defaults to FALSE.

matrix.type

Either "adjacency" or "edgelist".

vertex.names

Optional. A vector or list of vertex names.

vertex.attr

Optional. Additional vertex attributes.

dual.sign

Logical. Allow positive and negative edges simultaneously between the same pair.

timepoints

Optional. Pooling definition for dynamic networks.

tie.breaker

How to resolve ties when pooling signed matrices.

...

Additional arguments passed to 'network::network'.

Value

A signed network of class 'static.sign' or 'dynamic.sign'.

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

Description

Creates a composite network object from multiple signed networks, suitable for ERGM modeling. Can represent either a multilayer or dynamic signed network structure.

Usage

networks.sign(..., dynamic = FALSE, dual.sign = FALSE)

Arguments

...

One or more signed networks (objects of class "static.sign"), or a list of such networks.

dynamic

Logical. If TRUE, treat input as a dynamic network; otherwise as a multilayer network. Defaults to FALSE.

dual.sign

Logical. If TRUE, disables the layer fixing constraint. Defaults to FALSE.

Value

A combined network object of class "multi.sign" or "dynamic.sign", with the appropriate ERGM constraint formula.

Examples

data("tribes")
multi_net <- networks.sign(tribes, tribes)
dyn_net <- networks.sign(list(tribes, tribes), dynamic = TRUE)

Non-edgewise shared enemies

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of non-edges in the network with exactly 'd[i]' shared enemies. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: nse(d, type="OTP", base=NULL)

Arguments

d

a vector of distinct integers

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Non-edgewise shared friends

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of non-edges in the network with exactly 'd[i]' shared friends. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: nsf(d, type="OTP", base=NULL)

Arguments

d

a vector of distinct integers

type

base

specify the base of the triad, either by '+' and '-' or 1 and -1

Shared partner types

Note

Visualization for Dynamic Signed Networks

Description

plot.dynamic.sign() visualizes a dynamic signed network over multiple timepoints.

Usage

## S3 method for class 'dynamic.sign'
plot(
  x,
  col_pos = "#008000",
  col_neg = "#E3000F",
  col_both = "#333333",
  alpha = 1,
  neg.lty = 1,
  both.lty = 1,
  inv_weights = TRUE,
  time = NULL,
  titles = x %n% "names",
  fix.pos = TRUE,
  coord = NULL,
  ...
)

Arguments

x

A signed network object of class dynamic.sign.

col_pos

Color for positive edges. Default is green.

col_neg

Color for negative edges. Default is red.

col_both

Color for edges that are both positive and negative. Default is darkgrey.

alpha

Numeric between 0 and 1 for edge transparency. Default is 1 (fully opaque).

neg.lty

Line type for negative edges. Default is "solid". Other options are "dotted" and "dashed".

both.lty

Line type for edges that are both positive and negative. Default is "solid". Other options are "dotted" and "dashed".

inv_weights

Logical. If TRUE, edge weights are inverted (1/weights) so positive edges pull nodes closer together. Default is TRUE.

time

A vector of integers indicating which timepoints should be visualized. Defaults to all.

titles

A character vector of names for the timepoints.

fix.pos

Logical. If TRUE, the layout is fixed across timepoints based on the first timepoint. Default is TRUE.

coord

Optional matrix of coordinates for node positions. If NULL, layout is computed using stress majorization.

...

Additional arguments passed to the plot function.

Value

A list of plots, one for each selected timepoint.

Layout

Uses a force-directed graph layout based on stress majorization, implemented in the graphlayouts package via layout_with_stress(). Similar to Kamada-Kawai, but generally faster and with better results.

Visualization for Signed Networks

Description

Functions to visualize signed networks in static or dynamic form.

Usage

## S3 method for class 'static.sign'
plot(
  x,
  col_pos = "#008000",
  col_neg = "#E3000F",
  col_both = "#333333",
  alpha = 1,
  neg.lty = 1,
  both.lty = 1,
  inv_weights = TRUE,
  coord = NULL,
  ...
)

Arguments

x

A signed network object of class static.sign.

col_pos

Color for positive edges. Default is green.

col_neg

Color for negative edges. Default is red.

col_both

Color for edges that are both positive and negative. Default is darkgrey.

alpha

Numeric between 0 and 1 for edge transparency. Default is 1 (fully opaque).

neg.lty

Line type for negative edges. Default is "solid". Other options are "dotted" and "dashed".

both.lty

Line type for edges that are both positive and negative. Default is "solid". Other options are "dotted" and "dashed".

inv_weights

Logical. If TRUE, edge weights are inverted (1/weights) so positive edges pull nodes closer together. Default is TRUE.

coord

Optional matrix of coordinates for node positions. If NULL, layout is computed using stress majorization.

...

Additional arguments passed to the plot function.

Value

A plot of the signed network.

Layout

Static signed networks

plot.static.sign() visualizes a single (static) signed network.

References

Gansner ER, Koren Y, North S (2004). “Graph drawing by stress majorization.” In International Symposium on Graph Drawing, 239–250. Springer.

Examples

data("tribes")
plot(tribes, col_pos = "green", col_neg = "red")

Pool Timepoints of a Dynamic Signed Network

Description

Aggregates timepoints of a dynamic signed network into pooled periods using majority-vote logic, with configurable tie-breaking.

Usage

pool.sign(
  net,
  timepoints,
  tie.breaker = c("zero", "positive", "negative", "first", "last"),
  dual.sign = FALSE
)

Arguments

net

A dynamic signed network of class 'dynamic.sign'.

timepoints

Either an integer specifying the number of equal-sized groups, or a list of integer vectors specifying which timepoints belong to each group. E.g., 'list(c(1,2,3), c(4,5,6))' or '3' for three equal groups.

tie.breaker

How to resolve ties when positive and negative counts are equal. One of '"zero"' (default), '"positive"', '"negative"', '"first"', '"last"'.

dual.sign

Logical. Allow positive and negative edges simultaneously between the same pair. Defaults to FALSE.

Value

A dynamic signed network of class 'dynamic.sign' with one network per pooled group, or a 'static.sign' network if only one group results.

Conflict Events in Syrian Civil War

Description

A dynamic network of combat events in the Syrian civil war between 2017 and 2025. The raw data comes from the Armed Conflict Location & Event Data Project Raleigh et al. (2010).

Format

An undirected dynamic.sign object with no loops and eight timepoints.

References

Fritz C, Mehrl M, Thurner PW, Kauermann G (2023). “All that glitters is not gold: Relational events models with spurious events.” Network Science, 11(2), 184–204., Raleigh C, Linke r, Hegre H, Karlsen J (2010). “Introducing ACLED: An armed conflict location and event dataset.” Journal of peace research, 47(5), 651–660.

Examples

data(rebels)

Conflict Events in Syrian Civil War

Description

A pooled dynamic network of combat events in the Syrian civil war between 2017 and 2019 with 4 timepoints. The raw data comes from the Armed Conflict Location & Event Data Project Raleigh et al. (2010).

Format

An undirected dynamic.sign object with no loops and eight timepoints.

References

Examples

data(rebels)

Statnet Control

Description

A utility to facilitate argument completion of control lists, reexported from 'statnet.common'.

Currently recognised control parameters

This list is updated as packages are loaded and unloaded.

Package ergm

control.ergm: drop, init, init.method, main.method, force.main, main.hessian, checkpoint, resume, MPLE.samplesize, init.MPLE.samplesize, MPLE.type, MPLE.maxit, MPLE.nonvar, MPLE.nonident, MPLE.nonident.tol, MPLE.covariance.samplesize, MPLE.covariance.method, MPLE.covariance.sim.burnin, MPLE.covariance.sim.interval, MPLE.check, MPLE.constraints.ignore, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.interval, MCMC.burnin, MCMC.samplesize, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.return.stats, MCMC.runtime.traceplot, MCMC.maxedges, MCMC.addto.se, MCMC.packagenames, SAN.maxit, SAN.nsteps.times, SAN, MCMLE.termination, MCMLE.maxit, MCMLE.conv.min.pval, MCMLE.confidence, MCMLE.confidence.boost, MCMLE.confidence.boost.threshold, MCMLE.confidence.boost.lag, MCMLE.NR.maxit, MCMLE.NR.reltol, obs.MCMC.mul, obs.MCMC.samplesize.mul, obs.MCMC.samplesize, obs.MCMC.effectiveSize, obs.MCMC.interval.mul, obs.MCMC.interval, obs.MCMC.burnin.mul, obs.MCMC.burnin, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, obs.MCMC.impute.min_informative, obs.MCMC.impute.default_density, MCMLE.min.depfac, MCMLE.sampsize.boost.pow, MCMLE.MCMC.precision, MCMLE.MCMC.max.ESS.frac, MCMLE.metric, MCMLE.method, MCMLE.dampening, MCMLE.dampening.min.ess, MCMLE.dampening.level, MCMLE.steplength.margin, MCMLE.steplength, MCMLE.steplength.parallel, MCMLE.sequential, MCMLE.density.guard.min, MCMLE.density.guard, MCMLE.effectiveSize, obs.MCMLE.effectiveSize, MCMLE.interval, MCMLE.burnin, MCMLE.samplesize.per_theta, MCMLE.samplesize.min, MCMLE.samplesize, obs.MCMLE.samplesize.per_theta, obs.MCMLE.samplesize.min, obs.MCMLE.samplesize, obs.MCMLE.interval, obs.MCMLE.burnin, MCMLE.steplength.solver, MCMLE.last.boost, MCMLE.steplength.esteq, MCMLE.steplength.miss.sample, MCMLE.steplength.min, MCMLE.effectiveSize.interval_drop, MCMLE.save_intermediates, MCMLE.nonvar, MCMLE.nonident, MCMLE.nonident.tol, SA.phase1_n, SA.initial_gain, SA.nsubphases, SA.min_iterations, SA.max_iterations, SA.phase3_n, SA.interval, SA.burnin, SA.samplesize, CD.samplesize.per_theta, obs.CD.samplesize.per_theta, CD.nsteps, CD.multiplicity, CD.nsteps.obs, CD.multiplicity.obs, CD.maxit, CD.conv.min.pval, CD.NR.maxit, CD.NR.reltol, CD.metric, CD.method, CD.dampening, CD.dampening.min.ess, CD.dampening.level, CD.steplength.margin, CD.steplength, CD.adaptive.epsilon, CD.steplength.esteq, CD.steplength.miss.sample, CD.steplength.min, CD.steplength.parallel, CD.steplength.solver, loglik, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...
control.ergm.bridge: bridge.nsteps, bridge.target.se, bridge.bidirectional, drop, MCMC.burnin, MCMC.burnin.between, MCMC.interval, MCMC.samplesize, obs.MCMC.burnin, obs.MCMC.burnin.between, obs.MCMC.interval, obs.MCMC.samplesize, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...
control.ergm3: drop, init, init.method, main.method, force.main, main.hessian, checkpoint, resume, MPLE.samplesize, init.MPLE.samplesize, MPLE.type, MPLE.maxit, MPLE.nonvar, MPLE.nonident, MPLE.nonident.tol, MPLE.covariance.samplesize, MPLE.covariance.method, MPLE.covariance.sim.burnin, MPLE.covariance.sim.interval, MPLE.check, MPLE.constraints.ignore, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.interval, MCMC.burnin, MCMC.samplesize, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.return.stats, MCMC.runtime.traceplot, MCMC.maxedges, MCMC.addto.se, MCMC.packagenames, SAN.maxit, SAN.nsteps.times, SAN, MCMLE.termination, MCMLE.maxit, MCMLE.conv.min.pval, MCMLE.confidence, MCMLE.confidence.boost, MCMLE.confidence.boost.threshold, MCMLE.confidence.boost.lag, MCMLE.NR.maxit, MCMLE.NR.reltol, obs.MCMC.mul, obs.MCMC.samplesize.mul, obs.MCMC.samplesize, obs.MCMC.effectiveSize, obs.MCMC.interval.mul, obs.MCMC.interval, obs.MCMC.burnin.mul, obs.MCMC.burnin, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, obs.MCMC.impute.min_informative, obs.MCMC.impute.default_density, MCMLE.min.depfac, MCMLE.sampsize.boost.pow, MCMLE.MCMC.precision, MCMLE.MCMC.max.ESS.frac, MCMLE.metric, MCMLE.method, MCMLE.dampening, MCMLE.dampening.min.ess, MCMLE.dampening.level, MCMLE.steplength.margin, MCMLE.steplength, MCMLE.steplength.parallel, MCMLE.sequential, MCMLE.density.guard.min, MCMLE.density.guard, MCMLE.effectiveSize, obs.MCMLE.effectiveSize, MCMLE.interval, MCMLE.burnin, MCMLE.samplesize.per_theta, MCMLE.samplesize.min, MCMLE.samplesize, obs.MCMLE.samplesize.per_theta, obs.MCMLE.samplesize.min, obs.MCMLE.samplesize, obs.MCMLE.interval, obs.MCMLE.burnin, MCMLE.steplength.solver, MCMLE.last.boost, MCMLE.steplength.esteq, MCMLE.steplength.miss.sample, MCMLE.steplength.min, MCMLE.effectiveSize.interval_drop, MCMLE.save_intermediates, MCMLE.nonvar, MCMLE.nonident, MCMLE.nonident.tol, SA.phase1_n, SA.initial_gain, SA.nsubphases, SA.min_iterations, SA.max_iterations, SA.phase3_n, SA.interval, SA.burnin, SA.samplesize, CD.samplesize.per_theta, obs.CD.samplesize.per_theta, CD.nsteps, CD.multiplicity, CD.nsteps.obs, CD.multiplicity.obs, CD.maxit, CD.conv.min.pval, CD.NR.maxit, CD.NR.reltol, CD.metric, CD.method, CD.dampening, CD.dampening.min.ess, CD.dampening.level, CD.steplength.margin, CD.steplength, CD.adaptive.epsilon, CD.steplength.esteq, CD.steplength.miss.sample, CD.steplength.min, CD.steplength.parallel, CD.steplength.solver, loglik, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...
control.gof.ergm: nsim, MCMC.burnin, MCMC.interval, MCMC.batch, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT
control.gof.formula: nsim, MCMC.burnin, MCMC.interval, MCMC.batch, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT
control.logLik.ergm: bridge.nsteps, bridge.target.se, bridge.bidirectional, drop, MCMC.burnin, MCMC.interval, MCMC.samplesize, obs.MCMC.samplesize, obs.MCMC.interval, obs.MCMC.burnin, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...
control.san: SAN.maxit, SAN.tau, SAN.invcov, SAN.invcov.diag, SAN.nsteps.alloc, SAN.nsteps, SAN.samplesize, SAN.prop, SAN.prop.weights, SAN.prop.args, SAN.packagenames, SAN.ignore.finite.offsets, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT
control.simulate: MCMC.burnin, MCMC.interval, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...
control.simulate.ergm: MCMC.burnin, MCMC.interval, MCMC.scale, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...
control.simulate.formula: MCMC.burnin, MCMC.interval, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...
control.simulate.formula.ergm: MCMC.burnin, MCMC.interval, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

Description

A data frame containing binary indicators for whether each faction in the Syrian civil war is sponsored by a common external actor.

Format

A matrix with 68 rows and 68 columns.

References

Fritz C, Mehrl M, Thurner PW, Kauermann G (2023). “All that glitters is not gold: Relational events models with spurious events.” Network Science, 11(2), 184–204.

Examples

data(sponsor)

Network Attributes for Signed Networks

Description

Print descriptive statistics of a signed network.

Usage

## S3 method for class 'static.sign'
summary(object, ...)

## S3 method for class 'dynamic.sign'
summary(object, time = NULL, ...)

Arguments

object

A signed network object of class dynamic.sign.

...

Additional arguments.

time

Integer vector of timepoints to summarize. Defaults to all.

Value

A data frame or matrix with network attributes.

Static signed networks

summary.static.sign() summarizes a single (static) signed network.

Examples

data("tribes")
summary(tribes)

Summary formula method for dynamic signed networks

Description

Calculates statistics for dynamic.sign objects at specified timepoints.

Usage

## S3 method for class 'dynamic.sign'
summary_formula(object, at, ..., basis = NULL)

Arguments

object

A formula with a dynamic.sign network as LHS.

at

Numeric vector of timepoints. Defaults to all if missing.

...

Additional arguments passed to summary_formula for network objects.

basis

Optional dynamic.sign network. If NULL, uses LHS network.

Value

Matrix of statistics for each timepoint.

Read Highland Tribes

Description

A static network of political alliances and enmities among the 16 Gahuku-Gama sub-tribes of Eastern Central Highlands of New Guinea, documented by Read (1954).

Format

An undirected static.sign object with no loops.

References

Taken from UCINET IV, which cites the following: Hage P, Harary F (1983). Structural Models in Anthropology, Cambridge Studies in Social and Cultural Anthropology. Cambridge University Press. ISBN 9780521273114., Read KE (1954). “Cultures of the central highlands, New Guinea.” Southwestern Journal of Anthropology, 10(1), 1–43.

Examples


data(tribes)

Package {ergm.sign}

At least one common enemy

Description

Usage

Arguments

Shared partner types

Note

See Also

Keywords

At leat one common friend

Description

Usage

Arguments

Shared partner types

Note

See Also

Keywords

Evaluation of negative edges

Description

Usage

Arguments

Evaluation of positive edges

Description

Usage

Arguments

Delayed node matching on attribute (lag-1)

Description

Usage

Arguments

Delayed reciprocity

Description

Usage

Arguments

Construct a Signed Network Representation for ERGM

Description

Usage

Arguments

Value

See Also

Examples

Multilayer network to single layer network.

Description

Usage

Arguments

Value

See Also

Examples

Dyadwise shared enemies

Description

Usage

Shared partner types

Note

See Also

Keywords

Dyadwise shared friends

Description

Usage

Shared partner types

Note

See Also

Keywords

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

Description

Usage

Arguments

Value

Author(s)

Examples

Edgewise shared enemies

Description

Usage

Arguments

Shared partner types

Note

See Also

Keywords

Edgewise shared friends

Description

Usage

Arguments