mlergm: 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.

Version: 0.8
Depends: R (≥ 4.0.0), ergm (≥ 3.10.1), network (≥ 1.15)
Imports: parallel, Matrix, stringr, stats, GGally, ggplot2, cowplot, reshape2, plyr, methods, graphics, lpSolve, sna (≥ 2.4)
Suggests: RColorBrewer, knitr, rmarkdown
Published: 2021-08-23
DOI: 10.32614/CRAN.package.mlergm
Author: Jonathan Stewart [cre, aut], Michael Schweinberger [ctb]
Maintainer: Jonathan Stewart <jrstewart at>
License: GPL-3
NeedsCompilation: no
Citation: mlergm citation info
CRAN checks: mlergm results


Reference manual: mlergm.pdf
Vignettes: Tutorial: mlergm


Package source: mlergm_0.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlergm_0.8.tgz, r-oldrel (arm64): mlergm_0.8.tgz, r-release (x86_64): mlergm_0.8.tgz, r-oldrel (x86_64): mlergm_0.8.tgz
Old sources: mlergm archive


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