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btergm (version 1.9.3)

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Description

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.

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Install

install.packages('btergm')

Monthly Downloads

3,211

Version

1.9.3

License

GPL (>= 2)

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Repository

http://github.com/leifeld/btergm

Maintainer

Philip Leifeld

Last Published

August 24th, 2018

Functions in btergm (1.9.3)

checkdegeneracy

Degeneracy check for btergm and mtergm objects

Conduct Goodness-of-Fit Diagnostics on ERGMs, TERGMs, SAOMs, and logit models

Temporal dependencies for TERGMs

Plot and print methods for gof output.

simulate.btergm

Simulate new networks from btergm objects

Plot marginal effects for two-way interactions in ERGMs

Compute all dyadic edge probabilities for an ERGM or TERGM.

Extract the formula from a model.

Interpretation functions for ergm and btergm objects

TERGM by bootstrapped pseudolikelihood or MCMC MLE

Statistics for goodness-of-fit assessment of network models

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Classes "btergm" and "mtergm"