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mlergm
View on CRAN: Click
here
Download and install mlergm package within the R console
Install from CRAN:
install.packages("mlergm")
Install from Github:
library("remotes")
install_github("cran/mlergm") Install by package version:
library("remotes")
install_version("mlergm", "0.8.1") Attach the package and use:
library("mlergm")
Maintained by
Jonathan Stewart
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-12-03
Latest Update: 2025-05-22
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.
How to cite:
Jonathan Stewart (2018). mlergm: Multilevel Exponential-Family Random Graph Models. R package version 0.8.1, https://cran.r-project.org/web/packages/mlergm. Accessed 15 Jul. 2026.
Previous versions and publish date:
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Complete documentation for mlergm
Functions, R codes and Examples using
the mlergm R package
Some associated functions: classes . gof.mlergm . is.gof_mlergm . is.inCHv3.9 . is.mlergm . is.mlnet . mlergm . mlnet . plot.gof_mlergm . print.gof_mlergm . set_options . simulate_mlnet .
Some associated R codes: MCMC_sample.R . MCMLE.R . compute_initial_estimate.R . data.R . estimate_between_block.R . gof.mlergm.R . helper_functions.R . initialize_object.R . initialize_simulation_object.R . is.gof_mlergm.R . is.inCH-3.9.R . is.mlergm.R . is.mlnet.R . loglik_bridge.R . mlergm.R . mlnet.R . opt_fisher.R . plot.gof_mlergm.R . plot.mlnet.R . print.gof_mlergm.R . print.mlergm.R . set_options.R . simulate_mlnet.R . step_to_chull.R . summary.mlergm.R . Full mlergm package functions and examples
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