dnr: Simulate Dynamic Networks using Exponential Random Graph Models (ERGM) Family

Functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, <doi:10.1080/10618600.2019.1594834>.

Version:	0.3.5
Depends:	R (≥ 3.2.0), network, ergm
Imports:	sna, igraph, arm, glmnet
Suggests:	testthat, knitr
Published:	2020-11-30
Author:	Abhirup Mallik [aut, cre], Zack Almquist [aut]
Maintainer:	Abhirup Mallik <abhirupkgp at gmail.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	dnr results

Documentation:

Reference manual:	dnr.pdf
Vignettes:	Dynamic Network Regression Using dnr

Downloads:

Package source:	dnr_0.3.5.tar.gz
Windows binaries:	r-devel: dnr_0.3.5.zip, r-release: dnr_0.3.5.zip, r-oldrel: dnr_0.3.5.zip
macOS binaries:	r-release (arm64): dnr_0.3.5.tgz, r-oldrel (arm64): dnr_0.3.5.tgz, r-release (x86_64): dnr_0.3.5.tgz, r-oldrel (x86_64): dnr_0.3.5.tgz
Old sources:	dnr archive

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