jlsm provides a set of latent space models for jointly modeling
unipartite social networks with bipartite attribute networks. The latent space models are implemented using the
variational inference approach.
Latent space models for bipartite networks: the function blsm implements the bipartite latent space model (BLSM) outlined in Wang et al. (2021) using variational inference and squared Euclidian distance; the function
aplsm implements person and attribute latent space model (APLSM) introduced by
Wang et.al (2021).
These models assume that the person and attribute information can be summarized by latent person and attribute variables.
Both the Euclidean distances and the vector distances are used to describe relationships among persons and between persons and attributes.
Wang, S. S., Paul, S., Logan, J., & De Boeck, P. (2019). Joint analysis of social and item response networks with latent space models. arXiv preprint arXiv:1910.12128.