Latent Variable Models for Networks

Latent variable models for network data using fast inferential procedures.

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 (2016). 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.



Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2.1 by Isabella Gollini, 4 months ago

Report a bug at

Browse source code at

Authors: Isabella Gollini [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports igraph, grDevices, graphics, ellipse, stats, utils

Depends on MASS, ergm, network

See at CRAN