The FPCdpca Criterion on Distributed Principal Component Analysis
Overview
We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2025) doi:10.1016/j.physa.2024.130308.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 10
- Examples that run
- 100%10 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 20%
Downloads
Dependencies
Installing FPCdpca adds 2 packages (0 with compiled code, 3.3 MB of source tarballs). Heaviest: rsvd 3.3 MB, matrixcalc 30 KB.
Code & Tests
Datasets
People & History
Maintainer since 0.1.0 (2024)
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- RR 4.6.0 released · 2026-04-24
- 0.4.0Latest
- 0.3.02025-05-09 · diff ↗
- RR 4.5.0 released · 2025-04-11
- 0.2.0
- 0.1.02024-05-27
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-05-27
- Total releases
- 4 / 2 yrs
- License
- Apache License (== 2.0)
- Minimum R
- ≥ 3.5.0
- Bundled data
- 2.0 MB / 3 files
- Download size
- 2.0 MB
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