Exponential Random Graph Models for Small Networks
An alternative to as.matrix to retrieve adjacency matrix fast
Utility to benchmark expression in R
Block-diagonal models using ergm
Check the convergence of ergmito estimates
Count Network Statistics
Estimation of ERGMs using Maximum Likelihood Estimation (MLE)
Bootstrap of ergmito
Processing formulas in ergmito
Goodness of Fit diagnostics for ERGMito models
Vectorized calculation of ERGM exact log-likelihood
Extract function to be used with the texreg package.
Geodesic distance matrix (all pairs)
Extract a submatrix from a network
Manipulation of network objects
Creates a new ergmito_ptr
ERGMito sampler
Utility functions to query network dimensions
Function to visualize the optimization surface
Power set of Graphs of size n
Prediction method for ergmito objects
Random Bernoulli graph
Compare pairs of networks to see if those came from the same distribut...
Draw samples from a fitted ergmito model
Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.