Positional Estimation within the Latent Space : Nlodel for Networks 1

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network make modeling data of this type difficult. In this paper we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. We also provide a development of the implementation of this model via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data, and to visualize the results.