Multilevel Exponential-Family Random Graph Models
Evaluate the goodness-of-fit of an estimated model.
Check if object is of class gof_mlergm
Determine whether a vector is in the closure of the convex hull of som...
Check if the object is of class mlergm
Check if object is of class mlnet
Multilevel Exponential-Family Random Graph Models
Multilevel Network
Plot goodness-of-fit results
Print summary of a gof_mlergm object.
Set and adjust options and settings.
Simulate a multilevel network
Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.