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