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btergm

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

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. As described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

Functions (26)

adjust

Adjust the dimensions of a source object to the dimensions of a target object

alliances

Longitudinal international defense alliance network, 1981–2000

btergm

Estimate a TERGM by MPLE with temporal bootstrapping

btergm-class

An S4 class to represent a fitted TERGM by bootstrapped MPLE

btergm-package

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

checkdegeneracy

Check for degeneracy in fitted TERGMs

chemnet

German Toxic Chemicals Policy Network in the 1980s (Volker Schneider)

createBtergm

Constructor for btergm objects

createMtergm

Constructor for mtergm objects

createTbergm

Constructor for tbergm objects

edgeprob

Create all predicted tie probabilities using MPLE

getformula

Extract the formula from a model

gof

Goodness-of-fit diagnostics for ERGMs, TERGMs, SAOMs, and logit models

gof-plot

Plot and print methods for GOF output

gof-statistics

Statistics for goodness-of-fit assessment of network models

handleMissings

Handle missing data in matrices

interpret

Micro-Level Interpretation of (T)ERGMs

knecht

Longitudinal classroom friendship network and behavior (Andrea Knecht)

marginalplot

Plot marginal effects for two-way interactions in (T)ERGMs

mtergm

Estimate a TERGM by MCMC-MLE

mtergm-class

An S4 Class to represent a fitted TERGM by MCMC-MLE

simulate.btergm

Simulate Networks from a btergm Object

tbergm

Estimate a TERGM using Bayesian estimation

tbergm-class

An S4 class to represent a fitted TERGM using Bayesian estimation

tergm-terms

Temporal dependencies for TERGMs

tergmprepare

Prepare data structure for TERGM estimation, including composition change

btergm

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

v1.10.3
GPL (>= 2)
Authors
Philip Leifeld [aut, cre], Skyler J. Cranmer [ctb], Bruce A. Desmarais [ctb]
Initial release
2021-06-24

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