btergm
Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.
- Version1.11.1
- R versionR (≥ 3.5)
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- btergm citation info
- Last release03/19/2025
Team
Philip Leifeld
MaintainerShow author detailsSkyler J. Cranmer
Show author detailsRolesContributorBruce A. Desmarais
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 3,324 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 110 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 32,703 times in the last 365 days. The downloads are officially high enough to crash an underfunded departmental server. Quite an accomplishment! The day with the most downloads was Feb 05, 2025 with 264 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports9 packages
- Suggests6 packages
- Reverse Imports2 packages
- Reverse Suggests1 package
- Reverse Enhances1 package