Exponential-Family Random Network Models
Convert an Rcpp_UndirectedNet to a network object
Calculate network statistics
Internal symbols
Access ERNM parameters
Creates a C++ representation of an ERNM model
Create a C++ MCMC sampler
DirectedNet class
Goodness of fit for ERNM model
ERNM formula
ERNM model terms
Fits an ERNM model
Fit an ernm
CPP model objects
Create an ERNM package skeleton
Metropolis samplers
Subsetting and assignment for ernm network objects
Likelihood for a fully observed ernm
creates an ERNM likelihood model
MCMC approximate log-likelihood
Likelihood for an ernm with missing data
MCMC effective sample size
MCMC standard error by batch
Creates an ERNM likelihood model with missing data
Plot an ernm object
Print ernm object
Print a ERNM summary object
Register setter methods for Rcpp net objeccts
Register statistics
runErnmCppTests
Simulate statistics
Summary for ernm object
Ernm likelihood for a TaperedModel
UndirectedNet class
Parameter covariance matrix
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>.