gdim: Estimate Graph Dimension using Cross-Validated Eigenvalues
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.
| Version: |
0.1.0 |
| Depends: |
Matrix, R (≥ 3.5) |
| Imports: |
dplyr, ggplot2, irlba, magrittr, methods, progress, rlang, stats, tibble |
| Suggests: |
epca, fastRG, testthat (≥ 3.0.0) |
| Published: |
2023-09-05 |
| Author: |
Fan Chen  [aut],
Alex Hayes [cre,
aut, cph],
Karl Rohe [aut] |
| Maintainer: |
Alex Hayes <alexpghayes at gmail.com> |
| BugReports: |
https://github.com/RoheLab/gdim/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/RoheLab/gdim, https://rohelab.github.io/gdim/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
gdim results |
Documentation:
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