mlergm v0.2


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Multilevel Exponential-Family Random Graph Models

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.

Functions in mlergm

Name Description
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.
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License GPL-3
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
VignetteBuilder knitr
NeedsCompilation no
Packaged 2019-05-15 18:47:11 UTC; jstew
Repository CRAN
Date/Publication 2019-05-15 19:30:03 UTC

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