lvm4net provides a range of tools for latent variable models for network data. Most of the models are implemented using a fast variational inference approach. Latent space models for binary networks: the function lsm implements the latent space model (LSM) introduced by Hoff et al. (2002) using a variational inference and squared Euclidian distance; the function lsjm implements latent space joint model (LSJM) for multiplex networks introduced by Gollini and Murphy (2014). These models assume that each node of a network has a latent position in a latent space: the closer two nodes are in the latent space, the more likely they are connected. Functions for binary bipartite networks will be added soon.

• Gollini, I., and Murphy, T. B. (to appear), "Joint Modelling of Multiple Network Views"", Journal of Computational and Graphical Statistics, arXiv:1301.3759.

• Hoff, P., Raftery, A., and Handcock, M. (2002), "Latent Space Approaches to Social Network Analysis", Journal of the American Statistical Association, 97, 1090--1098