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btergm (version 1.5.2)

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

1.5.2

License

GPL (>= 2)

Maintainer

Philip Leifeld

Last Published

August 5th, 2015

Functions in btergm (1.5.2)

adjust

Adjust the dimensions of a matrix to the dimensions of another matrix
btergm-class

Class "btergm"
btergm

TERGM by bootstrapped pseudolikelihood or MCMC MLE
handleMissings

Handle missing data in matrices.
interpret

Interpretation functions for ergm and btergm objects
simulate.btergm

Simulate new networks from btergm objects
plot.btergmgof

Plot or print btergmgof objects
getformula

Extract the formula from a model.
timecov

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

Preprocess lists of network matrices for use with btergm
btergm-package

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
gof-methods

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