Latent Variable Models for Networks

Latent variable models for network data using fast inferential procedures. For more information please visit: < http://igollini.github.io/lvm4net/>.


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.


References

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Reference manual

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install.packages("lvm4net")

0.3 by Isabella Gollini, a month ago


http://github.com/igollini/lvm4net


Report a bug at http://github.com/igollini/lvm4net/issues


Browse source code at https://github.com/cran/lvm4net


Authors: Isabella Gollini [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports igraph, grDevices, graphics, ellipse, stats, utils, glmmML, mvtnorm, corpcor

Depends on MASS, ergm, network

Suggests knitr, rmarkdown, manet, colorspace


See at CRAN