dnr
0.3.6Simulate Dynamic Networks using Exponential Random Graph Models (ERGM) Family
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Overview
About
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>.
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- NOTE2026-03-105 OK · 9 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
Documentation
READMEYes · 184 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo
- Examples that run
- 54%
- Documented parameters
- 92%
- Return-value docs
- 92%
- References docs
- 0%
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Also on152 r2u28 autocran
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8 external dependencies (excludes base and recommended)
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Package Timeline
4 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.3.6Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.3.52020-11-30 · diff ↗
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.3.42018-07-26 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 0.3.22018-02-23
- RR 3.4.0 released · 2017-04-21
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Package metadata
- First published
- 2018-02-23
- Total releases
- 4 / 8 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.2.0
- Bundled data
- 1.3 MB / 2 files
- Download size
- 1.1 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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