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.
- Version0.1.0
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release09/05/2023
Documentation
Team
Alex Hayes
MaintainerShow author detailsFan Chen
Show author detailsRolesAuthorKarl Rohe
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 158 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 1,981 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 23, 2024 with 24 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Depends1 package
- Imports7 packages
- Suggests3 packages