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.

GERGM: Estimation and Fit Diagnostics for Generalized Exponential Random Graph Models

Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models via Gibbs sampling or Metropolis Hastings with exponential down weighting.

NetworkInference: Inferring Latent Diffusion Networks

This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010). Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.