JANE
2.1.0Just Another Latent Space Network Clustering Algorithm
Overview
Fit latent space network cluster models using an expectation-maximization algorithm. Enables flexible modeling of unweighted or weighted network data (with or without noise edges), supporting both directed and undirected networks (with or without degree and strength heterogeneity). Designed to handle large networks efficiently, it allows users to explore network structure through latent space representations, identify clusters (i.e., community detection) within network data, and simulate networks with varying clustering, connectivity patterns, and noise edges. Methodology for the implementation is described in Arakkal and Sewell (2025) doi:10.1016/j.csda.2025.108228.
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Health
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 0%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 17%
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2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/a1arakkal/jane on 2026-07-19.
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Code & Tests
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6 releases. Pick two to compare their code metrics. R releases are shown for context.
Package metadata
- First published
- 2024-11-14
- Total releases
- 6 / 2 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 124 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet