gdim
0.1.1Estimate Graph Dimension using Cross-Validated Eigenvalues
Overview
Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. The training graph is used to estimate eigenvectors, and the test graph is used to evaluate the correlation between the training eigenvectors and the eigenvectors of the test graph. The correlations follow a simple central limit theorem that can be used to estimate graph dimension via hypothesis testing, see Chen et al. (2021) doi:10.48550/arXiv.2108.03336 for details.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
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Checks run against github.com/rohelab/gdim on 2026-08-16.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.02023-09-05
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-09-05
- Total releases
- 2 / 3 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1
- Download size
- 26 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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