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gdim  

Estimate Graph Dimension using Cross-Validated Eigenvalues
View on CRAN: Click here


Download and install gdim package within the R console
Install from CRAN:
install.packages("gdim")

Install from Github:
library("remotes")
install_github("cran/gdim")

Install by package version:
library("remotes")
install_version("gdim", "0.1.0")



Attach the package and use:
library("gdim")
Maintained by
Alex Hayes
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-05
Latest Update: 2023-09-05
Description:
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) for details.
How to cite:
Alex Hayes (2023). gdim: Estimate Graph Dimension using Cross-Validated Eigenvalues. R package version 0.1.0, https://cran.r-project.org/web/packages/gdim. Accessed 21 Nov. 2024.
Previous versions and publish date:
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Complete documentation for gdim
Functions, R codes and Examples using the gdim R package
Some associated functions: eigcv . gdim-package . pipe . plot.eigcv . print.eigcv . 
Some associated R codes: eigcv.R . gdim-package.R . utils.R .  Full gdim package functions and examples
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