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btergm (version 1.6.5)

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

1.6.5

License

GPL (>= 2)

Maintainer

Philip Leifeld

Last Published

January 27th, 2016

Functions in btergm (1.6.5)

gofstatistics

Statistics for goodness-of-fit assessment of network models
gof-plot

Plot and print methods for gof output.
getformula

Extract the formula from a model.
btergm

TERGM by bootstrapped pseudolikelihood or MCMC MLE
checkdegeneracy

Degeneracy check for btergm objects
simulate.btergm

Simulate new networks from btergm objects
gof-methods

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

Create interaction terms between covariates and (transformations of) time for TERGMs
interpret

Interpretation functions for ergm and btergm objects
btergm-package

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
btergm-class

Classes "btergm" and "mtergm"