Package: ernm 1.0.5

ernm: Exponential-Family Random Network Models

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>.

Authors:Ian Fellows [aut], Duncan Clark [aut, cre]

ernm_1.0.5.tar.gz
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ernm_1.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
ernm/json (API)

# Install 'ernm' in R:
install.packages('ernm', repos = c('https://duncan-clark.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/duncan-clark/ernm/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

4.11 score 16 scripts 556 downloads 46 exports 38 dependencies

Last updated from:c5090b8f92. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK260
linux-devel-x86_64OK274
source / vignettesOK284
linux-release-arm64OK289
linux-release-x86_64OK277
macos-release-arm64OK171
macos-release-x86_64OK440
macos-oldrel-arm64OK148
macos-oldrel-x86_64OK339
windows-develOK284
windows-releaseOK289
windows-oldrelOK320
wasm-releaseOK169

Exports:_ernm_initStats_ernm_initToggles_rcpp_module_boot_ernmas.BinaryNetas.network.Rcpp_DirectedNetas.network.Rcpp_UndirectedNetcalculateStatisticscoef.ernmcreateCppModelcreateCppSamplerDirectedMetropolisHastingsDirectedModelDirectedNetDirectedTaperedModelernmernm_gofernmFiternmPackageSkeletonfullErnmLikelihoodFullErnmModelinitLatentlogLik.ernmmarErnmLikelihoodmcmcEssmcmcseMissingErnmModelplot.ernmplot.Rcpp_DirectedNetplot.Rcpp_UndirectedNetprint.ernmprint.ErnmSummaryregister_rcpp_net_methodsregisterDirectedStatisticregisterUndirectedStatisticrunErnmCppTestssimulateStatisticssummary.ernmtaperedErnmLikelihoodUndirectedCdSamplerUndirectedGibbsCdSamplerUndirectedGibbsCdSampler2UndirectedMetropolisHastingsUndirectedModelUndirectedNetUndirectedTaperedModelvcov.ernm

Dependencies:BHclicodacpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMatrixmomentsnetworkpillarpkgconfigpurrrR6RColorBrewerRcpprlangS7scalesstatnet.commonstringistringrtibbletidyrtidyselecttrustutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Convert an Rcpp_UndirectedNet to a network objectas.BinaryNet as.network.Rcpp_DirectedNet as.network.Rcpp_UndirectedNet BinaryNet DirectedNet plot.Rcpp_DirectedNet plot.Rcpp_UndirectedNet Rcpp_DirectedNet-class Rcpp_UndirectedNet-class UndirectedNet
Calculate network statisticscalculateStatistics
Access ERNM parameterscoef.ernm
Creates a C++ representation of an ERNM modelcreateCppModel
Create a C++ MCMC samplercreateCppSampler
DirectedNet classDirectedNet-class
Dutch School Datadutch_school
Fits an ERNM modelernm
Goodness of fit for ERNM modelernm_gof
ERNM formulaernm-formula
ERNM model termsernm-terms
Fit an ernmernmFit
Create an ERNM package skeletonernmPackageSkeleton
Subsetting and assignment for ernm network objectsextract-methods [,DirectedNet-method [,Rcpp_DirectedNet-method [,Rcpp_UndirectedNet-method [,UndirectedNet-method [<-,DirectedNet-method [<-,Rcpp_DirectedNet-method [<-,Rcpp_UndirectedNet-method [<-,UndirectedNet-method
Likelihood for a fully observed ernmfullErnmLikelihood
creates an ERNM likelihood modelFullErnmModel
MCMC approximate log-likelihoodlogLik.ernm
Likelihood for an ernm with missing datamarErnmLikelihood
MCMC effective sample sizemcmcEss
MCMC standard error by batchmcmcse
Creates an ERNM likelihood model with missing dataMissingErnmModel
Plot an ernm objectplot.ernm
Print ernm objectprint.ernm
Print a ERNM summary objectprint.ErnmSummary
Sampson's Monks Datasamplike sampson
Simulate statisticssimulateStatistics
Summary for ernm objectsummary.ernm
Ernm likelihood for a TaperedModeltaperedErnmLikelihood
UndirectedNet classUndirectedNet-class
Parameter covariance matrixvcov.ernm