Multilevel ExponentialFamily Random Graph Models
Estimates exponentialfamily 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 MonteCarlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponentialfamily 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 goodnessoffit of models and visualization of networks.
Details
License 
GPL3 
Encoding 
UTF8 
LazyData 
true 
RoxygenNote 
6.1.1 
VignetteBuilder 
knitr 
NeedsCompilation 
no 
Packaged 
20190515 18:47:11 UTC; jstew 
Repository 
CRAN 
Date/Publication 
20190515 19:30:03 UTC 
imports 
cowplot
,
GGally
,
ggplot2
,
graphics
,
Matrix
(>= 1.2.17)
,
methods
,
parallel
(>= 3.6.0)
,
plyr
,
reshape2
,
sna
(>= 2.4)
,
stats
,
stringr

depends 
ergm
(>= 3.10.1)
,
network
(>= 1.15)
,
R
(>= 3.5.0)

suggests 
knitr
,
RColorBrewer
,
rmarkdown

Contributors 
Michael Schweinberger

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