Package: gdim 0.1.0.9000

Alex Hayes

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.

Authors:Fan Chen [aut], Alex Hayes [cre, aut, cph], Karl Rohe [aut]

gdim_0.1.0.9000.tar.gz
gdim_0.1.0.9000.zip(r-4.7)gdim_0.1.0.9000.zip(r-4.6)gdim_0.1.0.9000.zip(r-4.5)
gdim_0.1.0.9000.tgz(r-4.6-any)gdim_0.1.0.9000.tgz(r-4.5-any)
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gdim_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gdim/json (API)

# Install 'gdim' in R:

install.packages('gdim', repos = c('https://rohelab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rohelab/gdim/issues

Pkgdown/docs site:https://rohelab.github.io

On CRAN:

3.54 score 7 stars 6 scripts 234 downloads 1 exports 32 dependencies

Last updated from:4b547b2419. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	148
source / vignettes	OK	177
linux-release-x86_64	NOTE	152
macos-release-arm64	NOTE	91
macos-oldrel-arm64	NOTE	90
windows-devel	NOTE	119
windows-release	NOTE	115
windows-oldrel	NOTE	81
wasm-release	OK	142