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btergm (version 1.9.13)

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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Version

Install

install.packages('btergm')

Monthly Downloads

2,612

Version

1.9.13

License

GPL (>= 2)

Issues

3

Pull Requests

0

Stars

17

Forks

10

Maintainer

Philip Leifeld

Last Published

October 26th, 2020

Functions in btergm (1.9.13)

gof-plot

Plot and print methods for gof output.
simulate.btergm

Simulate new networks from btergm objects
tergm-terms

Temporal dependencies for TERGMs
interpret

Interpretation functions for ergm and btergm objects
gofstatistics

Statistics for goodness-of-fit assessment of network models
marginalplot

Plot marginal effects for two-way interactions in ERGMs
btergm

TERGM by bootstrapped pseudolikelihood or MCMC MLE
checkdegeneracy

Degeneracy check for btergm and mtergm objects
getformula

Extract the formula from a model.
edgeprob

Compute all dyadic edge probabilities for an ERGM or TERGM.
btergm-package

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
btergm-class

Classes "btergm" and "mtergm"
gof-methods

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