Package 'ergm.sign'

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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	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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

gof(model, ...)

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

Arguments

model	A fitted signed ERGM (SERGM) object of class "sign".
...	Additional arguments passed to methods.
nsim	Integer; number of simulated networks. Defaults to 200.
seed	Optional integer seed.

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	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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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	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 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	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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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.

---

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

---

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.

See Also

ergmMPLE, ergm, glm

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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

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.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

None

---

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",
  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.
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",
  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.
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

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.

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.

See Also

UnLayer, layout_with_stress

Examples

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

---

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

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)

---

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"'.

See Also

[Layer()] for the underlying multilayer construction; ['ergm'][ergm::ergm] for model fitting on the resulting network.

Examples

# 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))

---

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, ...

See Also

[statnet.common::snctrl()]

---

Common Sponsor Data for Syrian Civil War Factions

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)

---

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.

---

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.

See Also

network.sign, UnLayer

Examples

data("tribes")
summary(tribes)

---

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)

---

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.

See Also

network.sign

Examples

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

---