BLSM
0.1.0Bayesian Latent Space Model
Overview
Provides a Bayesian latent space model for complex networks, either weighted or unweighted. Given an observed input graph, the estimates for the latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm. The overall likelihood of the graph depends on a fundamental probability equation, which is defined so that ties are more likely to exist between nodes whose latent space coordinates are close. The package is mainly based on the model by Hoff, Raftery and Handcock (2002) doi:10.1198/016214502388618906 and contains some extra features (e.g., removal of the Procrustean step, weights implemented as coefficients of the latent distances, 3D plots). The original code related to the above model was retrieved from https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/. Users can inspect the MCMC simulation, create and customize insightful graphical representations or apply clustering techniques.
Install
Health
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- NOTE2026-04-220 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-180 OK · 13 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-100 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 67%
- References docs
- 6%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2018-04-26
- Total releases
- 1 / 8 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.3.0
- Bundled data
- 84 KB / 3 files
- Download size
- 104 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet