ddpca
Diagonally Dominant Principal Component Analysis
Description
Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.
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Last year
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0
Last 30 days
25
Last 90 days
100
Last year
Trend: -100% (30d vs prior 30d)
0
Last 7 days
4
Last 30 days
0
All-time
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: ‘Fan Yang <fyang1@uchicago.edu>’ The Description field contains <arXiv:1906.00051>) by decomposing a large covariance matrix into a Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Fan Yang <fyang1@uchicago.edu>’ The Description field contains <arXiv:1906.00051>) by decomposing a large covariance matrix into a 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: ‘Fan Yang <fyang1@uchicago.edu>’ The Description field contains <arXiv:1906.00051>) by decomposing a large covariance matrix into a Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Fan Yang <fyang1@uchicago.edu>’ The Description field contains <arXiv:1906.00051>) by decomposing a large covariance matrix into a Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
Code
Structure
Lines of code
716
Files
19
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
7
Internal functions
0
Recent export changes
Testing & CI
Has tests
No
Test-to-code ratio
0.00
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
2
First release
2019-07-19
Latest release
2019-09-14
Avg cadence
57 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/cran-code-metrics.