mlergm v0.2

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by Jonathan Stewart View Source

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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Vignettes of mlergm

Name
mlergm_tutorial.Rmd
vig_data.rda
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Details

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

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