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) <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 ORCID iD [aut], Alex Hayes ORCID iD [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:

Reference manual: gdim.pdf

Downloads:

Package source: gdim_0.1.0.tar.gz
Windows binaries: r-devel: gdim_0.1.0.zip, r-release: gdim_0.1.0.zip, r-oldrel: gdim_0.1.0.zip
macOS binaries: r-release (arm64): gdim_0.1.0.tgz, r-oldrel (arm64): gdim_0.1.0.tgz, r-release (x86_64): gdim_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=gdim to link to this page.