Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
24 available
Adjust the dimensions of a source object to the dimensions of a target...
An S4 class to represent a fitted TERGM by bootstrapped MPLE
Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelih...
Estimate a TERGM by MPLE with temporal bootstrapping
Check for degeneracy in fitted TERGMs
Swiss political science co-authorship network 2013
Constructor for btergm objects
Constructor for mtergm objects
Constructor for tbergm objects
Create all predicted tie probabilities using MPLE
Extract the formula from a model
Plot and print methods for GOF output
Statistics for goodness-of-fit assessment of network models
Goodness-of-fit diagnostics for ERGMs, TERGMs, SAOMs, and logit models
Handle missing data in matrices
Micro-Level Interpretation of (T)ERGMs
Plot marginal effects for two-way interactions in (T)ERGMs
An S4 Class to represent a fitted TERGM by MCMC-MLE
Estimate a TERGM by MCMC-MLE
Simulate Networks from a btergm Object
An S4 class to represent a fitted TERGM using Bayesian estimation
Estimate a TERGM using Bayesian estimation
Temporal dependencies for TERGMs
Prepare data structure for TERGM estimation, including composition cha...
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. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.