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leifeld / btergm

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
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complex-networks dynamic-analysis ergm estimation goodness-of-fit inference longitudinal-data network-analysis prediction tergm

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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 (MCMC MLE). Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs.

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Installation

The last stable release can be installed from CRAN:

install.packages("btergm")

To install the latest development version from GitHub, use the remotes package:

remotes::install_github("leifeld/btergm")

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Documentation

Documentation of the package is available as a JStatSoft article:

Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2018): Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software 83(6): 1-36. https://doi.org/10.18637/jss.v083.i06.