| Type: | Package |
| Title: | Exponential-Family Models for Signed Networks |
| Version: | 0.1.0 |
| 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, vegan, 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: | 2025-11-18 15:09:06 UTC; mschalberger |
| Author: | Marc Schalberger [cre], Cornelius Fritz [aut], Pavel Krivitsky [ctb] |
| Maintainer: | Marc Schalberger <m.schalberger@fu-berlin.de> |
| Repository: | CRAN |
| Date/Publication: | 2025-11-21 15:20:02 UTC |
Conduct Goodness-of-Fit Diagnostics for a Signed ERGM
Description
Computes the goodness-of-fit (GoF) for a fitted signed exponential random graph model (SERGM). The function simulates new networks using the fitted model and compares key network statistics from the observed network with those from the simulated ones.
Usage
GoF(model, nsim = 200, seed = NULL)
Arguments
model |
A fitted signed ERGM (SERGM) object. |
nsim |
Integer; number of simulated networks to generate. Defaults to 200. |
seed |
Optional integer seed for reproducibility. Passed to
|
Details
The following diagnostics are plotted:
Positive degree distribution
Negative degree distribution
Edgewise shared enemies distribution (positive edges)
Edgewise shared enemies distribution (negative edges)
Edgewise shared friends distribution (positive edges)
Edgewise shared friends distribution (negative edges)
Value
Produces six diagnostic boxplots comparing observed and simulated statistics for the fitted model.
See Also
Examples
data("tribes")
fit <- mple_sign(tribes ~ Pos(~edges) + Neg(~edges))
GoF(fit, nsim = 100)
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 edgewise shared enemies
Description
This term adds one network statistic to the model that counts the number of positive or negative edges in the current network whose endpoints had exactly 'd' shared enemies (common negative ties) in the previous network.
For directed networks, different definitions of shared enemies can be specified using the 'type' argument. For undirected networks, only one configuration applies.
Usage
# binary: delese(d = 1, base = "+", type = "OTP")
Arguments
d |
Integer. The exact number of shared enemies to count for edges in the current network. |
base |
Character indicating which edges in the current network are used as the base: '"+"' for positive ties or '"-"' for negative ties. |
type |
For directed networks, the definition of shared enemies:
Ignored for undirected networks. |
Details
For each edge in the current network (positive or negative, depending on 'base'), this term checks how many nodes were connected negatively to both endpoints in the previous network.
Delayed edgewise shared friends
Description
This term adds one network statistic to the model that counts the number of positive or negative edges in the current network whose endpoints had exactly 'd' shared friends (common positive ties) in the previous network.
For directed networks, different definitions of shared friends can be specified using the 'type' argument. For undirected networks, only one configuration applies.
Usage
# binary: delese(d = 1, base = "+", type = "OTP")
Arguments
d |
Integer. The exact number of shared friends to count for edges in the current network. |
base |
Character indicating which edges in the current network are used as the base: '"+"' for positive ties or '"-"' for negative ties. |
type |
For directed networks, the definition of shared friends:
Ignored for undirected networks. |
Details
For each edge in the current network (positive or negative, depending on 'base'), this term checks how many nodes were connected positively to both endpoints in the previous network.
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. |
Geometrically weighted delayed edgewise shared enemies
Description
This term calculates the number of shared enemies based on the previous network.
It then applies a geometric transformation to these counts to reduce the influence of large counts.
Specifically, if the decay parameter decay is provided, the weighting function used is
f(k; decay) = 1 - (1 - e^{-decay})^k,
where k is the count of shared partners and decay controls how quickly the weight decreases as counts increase.
Usage
# binary: gwdelese(decay, base, type = "OTP")
Arguments
decay |
Numeric decay parameter controlling weighting intensity. If |
base |
Character indicating which edges in the current network are used as the base: '"+"' for positive ties or '"-"' for negative ties. |
type |
Character specifying which shared partner pattern to use; one of
|
Geometrically weighted delayed edgewise shared friends
Description
This term calculates the number of shared friends based on the previous network.
It then applies a geometric transformation to these counts to reduce the influence of large counts.
Specifically, if the decay parameter decay is provided, the weighting function used is
f(k; decay) = 1 - (1 - e^{-decay})^k,
where k is the count of shared partners and decay controls how quickly the weight decreases as counts increase.
If decay is no
Usage
# binary: gwdelesf(decay, base, type = "OTP")
Arguments
decay |
Numeric decay parameter controlling weighting intensity. If |
base |
Character indicating which edges in the current network are used as the base: '"+"' for positive ties or '"-"' for negative ties. |
type |
Character specifying which shared partner pattern to use; one of
|
Default MH algorithm respecting the layer constraint
Description
Stratifies the population of dyads edge status: those having ties and those having no ties (hence T/NT). This is useful for improving performance in sparse networks, because it gives at least 50% chance of proposing a toggle of an existing edge.
Details
| Reference | Enforces | May_Enforce | Priority | Weight | Class |
|---|---|---|---|---|---|
| Bernoulli | fixL sparse | bd bdmax .dyads | 1 | TNT | cross-sectional |
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", neg.lty = 2)
Arguments
net |
A signed network object of class |
color_pos |
Color for positive edges. Default is '#008000'. |
color_neg |
Color for negative edges. Default is '#E3000F'. |
neg.lty |
Line type for negative edges. Default is 2. |
Value
Single layer network object or a list of network objects for dynamic.sign.
See Also
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", in_order=FALSE)
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
directed, undirected, binary
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", in_order=FALSE)
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
directed, undirected, binary
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.
Author(s)
Marc Schalberger
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, in_order=FALSE)
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
directed, undirected, binary
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, in_order=FALSE)
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
directed, undirected, binary
Logical layer constraint
Description
This layer-aware constraint limits the sample space to those networks for which the specified logical layers are unchanged
Usage
# fixL(Ls)
See Also
['ergmConstraint'] for index of constraints and hints currently visible to the package.
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", in_order=FALSE)
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
directed, undirected, binary
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", in_order=FALSE)
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
directed, undirected, binary
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, in_order=FALSE)
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 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
directed, undirected, binary
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, in_order=FALSE)
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 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
directed, undirected, binary
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, in_order=FALSE)
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
directed, undirected, binary
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, in_order=FALSE)
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
directed, undirected, binary
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, ...)
Arguments
formula |
An ERGM formula with the network on the left-hand side. |
control |
A list of control parameters for |
seed |
Optional integer to set the random seed for reproducibility when simulating networks for Godambe covariance estimation. |
... |
Additional arguments passed to |
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
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 |
dynamic |
Logical. If |
dual.sign |
Logical. If |
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, in_order=FALSE)
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
directed, undirected, binary
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, in_order=FALSE)
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
directed, undirected, binary
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",
neg.lty = 1,
inv_weights = TRUE,
time = NULL,
titles = NULL,
fix.pos = TRUE,
...
)
Arguments
x |
A signed network object of class |
col_pos |
Color for positive edges. Default is 'green3'. |
col_neg |
Color for negative edges. Default is 'red3'. |
neg.lty |
Line type for negative edges. 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. |
... |
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",
neg.lty = 1,
inv_weights = TRUE,
coord = NULL,
...
)
Arguments
x |
A signed network object of class |
col_pos |
Color for positive edges. Default is 'green3'. |
col_neg |
Color for negative edges. Default is 'red3'. |
neg.lty |
Line type for negative edges. 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
Examples
data("tribes")
plot(tribes, col_pos = "green", col_neg = "red")
Propose a randomly selected dyad to toggle, respecting the layer constraint
Description
Propose a randomly selected dyad to toggle
Details
| Reference | Enforces | May_Enforce | Priority | Weight | Class |
|---|---|---|---|---|---|
| Bernoulli | fixL | bd bdmax .dyads | 0 | random | cross-sectional |
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)
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.ergmdrop, 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.bridgebridge.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.ergm.godfatherterm.optionscontrol.ergm3drop, 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.ergmnsim, 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.MTcontrol.gof.formulansim, 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.MTcontrol.logLik.ergmbridge.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.sanSAN.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.MTcontrol.simulateMCMC.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.ergmMCMC.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.formulaMCMC.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.ergmMCMC.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, ...
Package ergm.multi
control.gofNnsim, obs.twostage, array.max, simulate, obs.simulate, parallel, parallel.type, parallel.version.check, parallel.inherit.MTcontrol.gofN.ergmnsim, obs.twostage, array.max, simulate, obs.simulate, parallel, parallel.type, parallel.version.check, parallel.inherit.MT
Package tergm
control.simulate.formula.tergmMCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenamescontrol.simulate.networkMCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop.form, MCMC.prop.diss, MCMC.prop.weights.form, MCMC.prop.weights.diss, MCMC.prop.args.form, MCMC.prop.args.diss, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenamescontrol.simulate.stergmMCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop.form, MCMC.prop.diss, MCMC.prop.weights.form, MCMC.prop.weights.diss, MCMC.prop.args.form, MCMC.prop.args.diss, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenamescontrol.simulate.tergmMCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenamescontrol.stergminit.form, init.diss, init.method, force.main, MCMC.prop.form, MCMC.prop.diss, MCMC.prop.weights.form, MCMC.prop.args.form, MCMC.prop.weights.diss, MCMC.prop.args.diss, MCMC.maxedges, MCMC.maxchanges, MCMC.packagenames, CMLE.MCMC.burnin, CMLE.MCMC.interval, CMLE.ergm, CMLE.form.ergm, CMLE.diss.ergm, CMLE.NA.impute, CMLE.term.check.override, EGMME.main.method, EGMME.initialfit.control, EGMME.MCMC.burnin.min, EGMME.MCMC.burnin.max, EGMME.MCMC.burnin.pval, EGMME.MCMC.burnin.add, MCMC.burnin, MCMC.burnin.mul, SAN.maxit, SAN.nsteps.times, SAN, SA.restarts, SA.burnin, SA.plot.progress, SA.max.plot.points, SA.plot.stats, SA.init.gain, SA.gain.decay, SA.runlength, SA.interval.mul, SA.init.interval, SA.min.interval, SA.max.interval, SA.phase1.minruns, SA.phase1.tries, SA.phase1.jitter, SA.phase1.max.q, SA.phase1.backoff.rat, SA.phase2.levels.max, SA.phase2.levels.min, SA.phase2.max.mc.se, SA.phase2.repeats, SA.stepdown.maxn, SA.stepdown.p, SA.stop.p, SA.stepdown.ct, SA.phase2.backoff.rat, SA.keep.oh, SA.keep.min.runs, SA.keep.min, SA.phase2.jitter.mul, SA.phase2.maxreljump, SA.guard.mul, SA.par.eff.pow, SA.robust, SA.oh.memory, SA.refine, SA.se, SA.phase3.samplesize.runs, SA.restart.on.err, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...control.tergminit, init.method, force.main, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.maxchanges, MCMC.packagenames, CMLE.MCMC.burnin, CMLE.MCMC.interval, CMLE.ergm, CMLE.NA.impute, CMLE.term.check.override, EGMME.main.method, EGMME.initialfit.control, EGMME.MCMC.burnin.min, EGMME.MCMC.burnin.max, EGMME.MCMC.burnin.pval, EGMME.MCMC.burnin.add, MCMC.burnin, MCMC.burnin.mul, SAN.maxit, SAN.nsteps.times, SAN, SA.restarts, SA.burnin, SA.plot.progress, SA.max.plot.points, SA.plot.stats, SA.init.gain, SA.gain.decay, SA.runlength, SA.interval.mul, SA.init.interval, SA.min.interval, SA.max.interval, SA.phase1.minruns, SA.phase1.tries, SA.phase1.jitter, SA.phase1.max.q, SA.phase1.backoff.rat, SA.phase2.levels.max, SA.phase2.levels.min, SA.phase2.max.mc.se, SA.phase2.repeats, SA.stepdown.maxn, SA.stepdown.p, SA.stop.p, SA.stepdown.ct, SA.phase2.backoff.rat, SA.keep.oh, SA.keep.min.runs, SA.keep.min, SA.phase2.jitter.mul, SA.phase2.maxreljump, SA.guard.mul, SA.par.eff.pow, SA.robust, SA.oh.memory, SA.refine, SA.se, SA.phase3.samplesize.runs, SA.restart.on.err, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MTcontrol.tergm.godfatherterm.options
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)
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, names = NULL, ...)
Arguments
object |
A signed network object of class |
... |
Additional arguments. |
time |
Integer vector of timepoints to summarize. Defaults to all. |
names |
Character vector of names for timepoints. If NULL, uses "Time 1", "Time 2", etc. |
Value
A data frame or matrix with network attributes.
Static signed networks
summary.static.sign() summarizes a single (static) signed network.
See Also
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)