Network event history analysis (NEHA), introduced by a Harden et al. (2023) is a methodology for modeling the diffusion of some attribute across units’ while simultaneously accountign for the effects of (1) observable covariates and (2) latent network ties between units. This package implements NEHA.
An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) (see also Harden and Desmarais, 2011) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012). These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) and Joshi & Mason (2008) that are included to facilitate examples from real-world analysis.
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.
Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models via Gibbs sampling or Metropolis Hastings with exponential down weighting.
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.
