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btergm (version 1.7.0)

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

7,463

Version

1.7.0

License

GPL (>= 2)

Maintainer

Philip Leifeld

Last Published

February 20th, 2016

Functions in btergm (1.7.0)

tergm-terms

Temporal dependencies for TERGMs
gofstatistics

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

Extract the formula from a model.
gof-plot

Plot and print methods for gof output.
simulate.btergm

Simulate new networks from btergm objects
interpret

Interpretation functions for ergm and btergm objects
btergm-package

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
btergm-class

Classes "btergm" and "mtergm"
timecov

Create interaction terms between covariates and (transformations of) time for TERGMs
gof-methods

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

Degeneracy check for btergm and mtergm objects
btergm

TERGM by bootstrapped pseudolikelihood or MCMC MLE