CASCORE
Covariate Assisted Spectral Clustering on Ratios of Eigenvectors
Description
Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) <arXiv:2306.15616>. In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.
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109
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Trend: -100% (30d vs prior 30d)
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autoCRAN-only: this name is served only by autoCRAN, so the count is exact.
CRAN Check Status
Show all 13 flavors
| Flavor | Status |
|---|---|
| r-devel-linux-x86_64-debian-clang | NOTE |
| r-devel-linux-x86_64-debian-gcc | NOTE |
| r-devel-linux-x86_64-fedora-clang | OK |
| r-devel-linux-x86_64-fedora-gcc | OK |
| r-devel-windows-x86_64 | OK |
| r-oldrel-macos-arm64 | OK |
| r-oldrel-macos-x86_64 | OK |
| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
| r-release-linux-x86_64 | OK |
| r-release-macos-arm64 | OK |
| r-release-macos-x86_64 | OK |
| r-release-windows-x86_64 | OK |
Check details (2 non-OK)
CRAN incoming feasibility
Maintainer: ‘Yaofang Hu <yaofangh@smu.edu>’ The Description field contains Wang (2022) <arXiv:2306.15616>. In addition to CASCORE, this package Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Yaofang Hu <yaofangh@smu.edu>’ The Description field contains Wang (2022) <arXiv:2306.15616>. In addition to CASCORE, this package Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
Check History
NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
CRAN incoming feasibility
Maintainer: ‘Yaofang Hu <yaofangh@smu.edu>’ The Description field contains Wang (2022) <arXiv:2306.15616>. In addition to CASCORE, this package Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Yaofang Hu <yaofangh@smu.edu>’ The Description field contains Wang (2022) <arXiv:2306.15616>. In addition to CASCORE, this package Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
Code
Structure
Lines of code
1,735
Files
36
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
8
Internal functions
8
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.31
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Return-value doc rate
100%
\dontrun example ratio
0%
Roxygen coverage
100%
Has pkgdown
No
NEWS present
No
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
–
System requirements
–
C++ standard
–
License
GPL-2
License flags
SPDX valid, OSI approved
History
Versions
3
First release
2022-08-17
Latest release
2023-07-02
Avg cadence
160 days
Cold removal rate
–
Dep drift
3
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/cran-code-metrics.