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mlergm (version 0.2)

Multilevel Exponential-Family Random Graph Models

Description

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) . The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.

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Version

Install

install.packages('mlergm')

Monthly Downloads

597

Version

0.2

License

GPL-3

Maintainer

Jonathan Stewart

Last Published

May 15th, 2019

Functions in mlergm (0.2)

simulate_mlnet

Simulate a multilevel network
mlergm

Multilevel Exponential-Family Random Graph Models
set_options

Set and adjust options and settings.
mlnet

Multilevel Network
is.gof_mlergm

Check if object is of class gof_mlergm
is.mlnet

Check if object is of class mlnet
print.gof_mlergm

Print summary of a gof_mlergm object.
plot.gof_mlergm

Plot goodness-of-fit results
classes

Polish school classes data set.
is.mlergm

Check if the object is of class mlergm
gof.mlergm

Evaluate the goodness-of-fit of an estimated model.