| Type: | Package |
| Title: | Exponential-Family Random Network Models |
| Version: | 1.0.1 |
| Date: | 2025-7-22 |
| Description: | Estimation of fully and partially observed Exponential-Family Random Network Models (ERNM). Exponential-family Random Graph Models (ERGM) and Gibbs Fields are special cases of ERNMs and can also be estimated with the package. Please cite Fellows and Handcock (2012), "Exponential-family Random Network Models" available at <doi:10.48550/arXiv.1208.0121>. |
| License: | LGPL-2.1 |
| Depends: | R (≥ 3.5.0), BH, methods, network, Rcpp |
| Imports: | dplyr, ggplot2, graphics, moments, rlang, stats, tidyr, trust |
| Suggests: | spelling, testthat |
| LinkingTo: | BH, Rcpp |
| Copyright: | 2014-2025 Ian Fellows |
| Encoding: | UTF-8 |
| Language: | en-US |
| LazyData: | true |
| LazyLoad: | yes |
| RcppModules: | ernm |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | yes |
| Packaged: | 2025-07-22 21:01:12 UTC; dac6 |
| Author: | Ian Fellows [aut], Duncan Clark [aut, cre] |
| Maintainer: | Duncan Clark <dac6@williams.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-22 21:22:27 UTC |
BinaryNet
Description
BinaryNet
DirectedNet Class
Description
An S4 (old-style) class representing a directed network.
Models
Description
Models
Metropolis Samplers
Description
Metropolis Samplers
creates an ERNM likelihood model
Description
creates an ERNM likelihood model
Usage
FullErnmModel(sampler, logLik, ...)
Arguments
sampler |
a sampler |
logLik |
a log likelihood function (optional) |
... |
additional parameters for the log likelihood |
Value
a FullyObservedModel object
creates an ERNM likelihood model
Description
creates an ERNM likelihood model
Usage
MissingErnmModel(observedSampler, unobservedSampler, ...)
Arguments
observedSampler |
a sampler |
unobservedSampler |
a sampler conditional upon the observed values |
... |
additional parameters for the log likelihood |
Value
a MarModel object
UndirectedNet Class
Description
An S4 (old-style) class representing an undirected network.
convert and network to either an UndirectedNet or DirectedNet object
Description
convert and network to either an UndirectedNet or DirectedNet object
Usage
as.BinaryNet(x, ...)
Arguments
x |
the object |
... |
unused |
Value
a BinaryNet object
Conversion to network object
Description
Conversion to network object
Usage
## S3 method for class 'DirectedNet'
as.network(x, ...)
Arguments
x |
the object |
... |
unused |
Value
a directed network object
convert and Rcpp_DirectedNet to a network object
Description
convert and Rcpp_DirectedNet to a network object
Usage
## S3 method for class 'Rcpp_DirectedNet'
as.network(x, ...)
Arguments
x |
the object |
... |
unused |
Value
a directed network object
convert and UndirectedNet to a network object
Description
convert and UndirectedNet to a network object
Usage
## S3 method for class 'Rcpp_UndirectedNet'
as.network(x, ...)
Arguments
x |
the object |
... |
unused |
Value
a undirected network object
Conversion to network object
Description
Conversion to network object
Usage
## S3 method for class 'UndirectedNet'
as.network(x, ...)
Arguments
x |
the object |
... |
unused |
Value
a undirected network object
calculate model statistics from a formula
Description
calculate model statistics from a formula
Usage
calculateStatistics(formula)
Arguments
formula |
An ernm formula |
Value
a list of statistics
Internal Symbols
Description
Internal symbols used to access compiles code.
creates a model
Description
creates a model
Usage
createCppModel(
formula,
ignoreMnar = TRUE,
cloneNet = TRUE,
theta = NULL,
modelArgs = list(modelClass = "Model")
)
Arguments
formula |
the model formula |
ignoreMnar |
ignore missing not at random offsets |
cloneNet |
should the network be cloned |
theta |
the model parameters. |
modelArgs |
additiional arguments for the model, e.g. tapering parameters |
Value
a Model object
create a sampler
Description
create a sampler
Usage
createCppSampler(
formula,
modelArgs = list(modelClass = "Model"),
dyadToggle = NULL,
dyadArgs = list(),
vertexToggle = NULL,
vertexArgs = list(),
nodeSamplingPercentage = 0.2,
ignoreMnar = TRUE,
theta = NULL,
...
)
Arguments
formula |
the model formula |
modelArgs |
additiional arguments for the model, e.g. tapering parameters |
dyadToggle |
the method of sampling to use. Defaults to alternating between nodal-tie-dyad and neighborhood toggling. |
dyadArgs |
list of args for dyad |
vertexToggle |
the method of vertex attribuate sampling to use. |
vertexArgs |
list of args for vertex |
nodeSamplingPercentage |
how often the nodes should be toggled |
ignoreMnar |
ignore missing not at random offsets |
theta |
parameter values |
... |
additional parameters to be passed to createCppModel |
Value
a MetropolisHastings object
Dutch School Data
Description
This dataset contains network and actor attributes collected in early adolescence. It is provided by Andrea Knecht and stored in the package.
Usage
data(dutch_school)
data("dutch_school")
Format
An object of class list of length 4.
Data taken from https
//www.stats.ox.ac.uk/~snijders/siena/tutorial2010_data.htm. Processed as undirected networks.
Source
Knecht, A. (2004). *Network and actor attributes in early adolescence*. DANS Data Station Social Sciences and Humanities. DOI: doi:10.17026/dans-z9b-h2bp.
References
Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010), Introduction to actor-based models for network dynamics, Social Networks 32, 44-60, http://dx.doi.org/10.1016/j.socnet.2009.02.004.
fits an ERNM model
Description
fits an ERNM model
Usage
ernm(
formula,
tapered = TRUE,
tapering_r = 3,
modelArgs = list(),
nodeSamplingPercentage = 0.2,
modelType = NULL,
likelihoodArgs = list(),
fullToggles = c("Compound_NodeTieDyad_Neighborhood", "DefaultVertex"),
missingToggles = c("Compound_NodeTieDyadMissing_NeighborhoodMissing", "VertexMissing"),
...
)
Arguments
formula |
model formula |
tapered |
should the model be tapered |
tapering_r |
the tapering parameter (tau = 1/(tapering_r^2 +5)) |
modelArgs |
additiional arguments for the model, e.g. tapering parameters that override the defaults |
nodeSamplingPercentage |
how often are nodal variates toggled |
modelType |
either FullErnmModel or MissingErnmModel if NULL will check for missingness |
likelihoodArgs |
additiional arguments for the ernmLikelihood |
fullToggles |
a character vector of length 2 indicating the dyad and vertex toggle types for the unconditional simulations |
missingToggles |
a character vector of length 2 indicating the dyad and vertex toggle types for the conditional simulations |
... |
additional parameters for ernmFit |
Value
a fitted model
fit an ernm model
Description
fit an ernm model
Usage
ernmFit(
sampler,
theta0,
mcmcBurnIn = 10000,
mcmcInterval = 100,
mcmcSampleSize = 10000,
minIter = 3,
maxIter = 40,
objectiveTolerance = 0.5,
gradTolerance = 0.25,
meanStats,
verbose = 1,
method = c("bounded", "newton")
)
Arguments
sampler |
the ErnmModel |
theta0 |
initial starting values |
mcmcBurnIn |
burn in |
mcmcInterval |
interval |
mcmcSampleSize |
sample size |
minIter |
minimum number of iterations |
maxIter |
maximum number of iterations |
objectiveTolerance |
convergance criteria on change in log likelihood ratio |
gradTolerance |
convergance criteria on scaled gradient |
meanStats |
if non-missing, these are the target statistics |
verbose |
level of verbosity 0, 1, or 2 |
method |
the optimization method to use |
Value
ernm object
Create an ERNM Package Skeleton
Description
Creates a skeleton for a package extending the ernm package by copying an example package.
Usage
ernmPackageSkeleton(path = ".")
Arguments
path |
A character string specifying the directory where the package skeleton will be created. |
Value
A logical value indicating whether the copy was successful.
Description
Goodness of fit plot for ERNM models, particularly suited for comparing models
Usage
ernm_gof(
models,
observed_network = NULL,
stats_formula,
style = "histogram",
scales = "fixed",
print = TRUE,
n_sim = 10000,
burnin = 10000,
interval = 100
)
Arguments
models |
named list of ernm models to be to be compared (can be length 1 |
observed_network |
the observed network |
stats_formula |
the formula for the statistics |
style |
the style of the plot, either 'histogram' or 'boxplot' |
scales |
the scales of the plot, either 'fixed' or 'free' |
print |
whether to print the plot |
n_sim |
the number of simulations to run |
burnin |
the burnin for the MCMC simulation |
interval |
the samplling interval for MCMC simualtion |
Value
A list containing goodness-of-fit plots and simulated statistics
Subsetting and assignment for Net objects
Description
These methods allow standard subsetting ('[') and assignment ('[<-') for 'DirectedNet' and 'UndirectedNet' objects.
Usage
## S4 method for signature 'Rcpp_DirectedNet'
x[i, j, ..., maskMissing = TRUE, drop = TRUE]
## S4 method for signature 'Rcpp_UndirectedNet'
x[i, j, ..., maskMissing = TRUE, drop = TRUE]
## S4 replacement method for signature 'Rcpp_DirectedNet'
x[i, j, ...] <- value
## S4 replacement method for signature 'Rcpp_UndirectedNet'
x[i, j, ...] <- value
Arguments
x |
A 'DirectedNet' or 'UndirectedNet' object. |
i, j |
Index vectors. |
... |
Currently unused. |
maskMissing |
Logical. Should missing values be masked by NA? |
drop |
Ignored (present for compatibility). |
value |
Values to assign (for '[<-' only). |
Value
A modified object or extracted submatrix depending on the method.
likelihood for a fully observed ernm
Description
likelihood for a fully observed ernm
Usage
fullErnmLikelihood(
theta,
sample,
theta0,
stats,
minEss = 5,
damping = 0.05,
method = c("cumulant", "sample"),
order = 3
)
Arguments
theta |
parameters |
sample |
mcmc sample |
theta0 |
parameter values which generated sample |
stats |
observed statistics |
minEss |
minimum effective sample size |
damping |
a damping parameter |
method |
cumulant generating function approximation |
order |
the ordering |
Value
a list with value, gradient, and hessian
likelihood for an ernm with missing data
Description
likelihood for an ernm with missing data
Usage
marErnmLikelihood(theta, sample, theta0, stats, minEss = 5, damping = 0.1)
Arguments
theta |
parameters |
sample |
mcmc sample |
theta0 |
parameter values which generated sample |
stats |
observed statistics |
minEss |
minimum effective sample size |
damping |
a damping parameter |
Value
a list with value, gradient, and hessian
MCMC Effective Sample Size
Description
Computes the effective sample size from a statistic vector.
Usage
mcmcEss(x)
Arguments
x |
A numeric vector. |
Value
A numeric value representing the effective sample size.
References
Kass, R. E., Carlin, B. P., Gelman, A., & Neal, R. M. (1998). "Markov Chain Monte Carlo in Practice: A Roundtable Discussion." *The American Statistician*, 52(2), 93-100. DOI: doi:10.2307/2685466
MCMC Standard Error by Batch
Description
Computes the MCMC standard error from a statistic vector using a batching method.
Usage
mcmcse(x, expon = 0.5)
Arguments
x |
A numeric vector of statistics. |
expon |
A numeric value controlling the batch size; default is 0.5. |
Value
A numeric value representing the estimated standard error.
plot an Rcpp_UnirectedNet object
Description
plot an Rcpp_UnirectedNet object
Usage
## S3 method for class 'Rcpp_DirectedNet'
plot(x, ...)
Arguments
x |
the object |
... |
additional parameters for plot.network |
Value
No return value, invisibly NULL
plot an UndirectedNet object
Description
plot an UndirectedNet object
Usage
## S3 method for class 'Rcpp_UndirectedNet'
plot(x, ...)
Arguments
x |
the object |
... |
additional parameters for plot.network |
Value
No return value, invisibly NULL
plot an ernm object
Description
plot an ernm object
Usage
## S3 method for class 'ernm'
plot(x, ...)
Arguments
x |
the object |
... |
unused |
Value
No return value, plots the likelihood history
Description
Usage
## S3 method for class 'ernm'
print(x, ...)
Arguments
x |
x |
... |
unused |
Value
No return value, prints summary
Register Statistics
Description
Register Statistics
Usage
registerDirectedStatistic
Value
no return value
Register setter methods for Rcpp net objeccts
Description
Register setter methods for Rcpp net objeccts
Usage
register_rcpp_net_methods()
Value
no return value
runErnmCppTests
Description
Runs the internal C++ tests for the ernm package.
Value
A logical value indicating whether all tests passed.
Examples
runErnmCppTests()
Sampson's Monks Data
Description
This dataset represents the social network of relationships among monks in a monastery, as studied by Samuel F. Sampson. The data were collected during a period of instability and document both positive and negative interactions among the monks.
Usage
data(samplike)
data("samplike")
Format
An object of class network of length 5.
Details
The study recorded friendships, antagonisms, and other social relationships among the monks before and after a significant schism occurred in the monastery.
NOTE COPIED FROM ERGM PACKAGE
Mislabeling in Versions Prior to 3.6.1: In ergm version
3.6.0 and earlier, the adjacency matrices of the datasets
reflected an older ordering of the names.
Source
Sampson, S. F. (1969). *Crisis in a cloister*. Unpublished Ph.D. dissertation, Cornell University.
References
White, H.C., Boorman, S.A. and Breiger, R.L. (1976). Social structure from multiple networks. I. Blockmodels of roles and positions. American Journal of Sociology, 81(4), 730-780.
See Also
florentine, network, plot.network, ergm
simulate statistics
Description
simulate statistics
Usage
simulateStatistics(
formula,
theta,
nodeSamplingPercentage = 0.2,
mcmcBurnIn = 10000,
mcmcInterval = 100,
mcmcSampleSize = 100,
ignoreMnar = TRUE,
modelArgs = list(modelClass = "Model"),
...
)
Arguments
formula |
the model formula |
theta |
model parameters |
nodeSamplingPercentage |
how often the nodes should be toggled |
mcmcBurnIn |
burn in |
mcmcInterval |
interval |
mcmcSampleSize |
sample size |
ignoreMnar |
ignore missing not at random offsets |
modelArgs |
additiional arguments for the model, e.g. tapering parameters |
... |
additional arguments to createCppSampler |
Value
a list of statistics
summary
Description
summary
Usage
## S3 method for class 'ernm'
summary(object, ...)
Arguments
object |
object |
... |
unused |
Value
a data frame summary of the model
(E(g(X)) - g(x_o)^2 for TaperedModel
Description
(E(g(X)) - g(x_o)^2 for TaperedModel
Usage
taperedErnmLikelihood(
theta,
centers,
tau,
sample,
theta0,
stats,
minEss = 5,
damping = 0.05
)
Arguments
theta |
parameters |
centers |
center of statistics |
tau |
tapering parameter |
sample |
mcmc sample |
theta0 |
parameter values which generated sample |
stats |
observed statistics |
minEss |
minimum effective sample size |
damping |
a damping parameter |
Value
a list with value, gradient, and hessian
parameter covariance matrix
Description
parameter covariance matrix
Usage
## S3 method for class 'ernm'
vcov(object, ...)
Arguments
object |
object |
... |
unused |
Value
covariance matrix