latentnet is a package to fit and evaluate statistical latent position
and cluster models for networks. Hoff, Raftery, and Handcock (2002)
suggested an approach to modeling networks based on positing the existence
of an latent space of characteristics of the actors. Relationships form as
a function of distances between these characteristics as well as functions
of observed dyadic level covariates. In latentnet social distances are
represented in a Euclidean space. It also includes a variant of the
extension of the latent position model to allow for clustering of the
positions developed in Handcock, Raftery, and Tantrum (2007). The package
implements Bayesian inference for the models based on an Markov chain
Monte Carlo algorithm. It can also compute maximum likelihood estimates
for the latent position model and a two-stage maximum likelihood method
for the latent position cluster model. For latent position cluster models,
the package provides a Bayesian way of assessing how many groups there
are, and thus whether or not there is any clustering (since if the
preferred number of groups is 1, there is little evidence for clustering).
It also estimates which cluster each actor belongs to. These estimates are
probabilistic, and provide the probability of each actor belonging to each
cluster. It computes four types of point estimates for the coefficients
and positions: maximum likelihood estimate, posterior mean, posterior mode
and the estimator which minimizes Kullback-Leibler divergence from the
posterior. You can assess the goodness-of-fit of the model via posterior
predictive checks. It has a function to simulate networks from a latent
position or latent position cluster model.