Differentially Private Principal Component Analysis Visualization
Overview
Provides tools for differentially private principal component analysis (PCA) visualization. It includes functions for estimating private principal component directions, constructing private scree and proportion of variance explained summaries, and visualizing two-dimensional PCA score summaries using additive and sparse histogram mechanisms. Group-wise score visualizations and an interactive 'shiny' app are also provided. Private principal component directions are based on Kim and Jung (2025) doi:10.1002/sam.70053. Private scree summaries use mechanisms motivated by Dwork and Roth (2014) doi:10.1561/0400000042, Ramsay and Spicker (2025) doi:10.48550/arXiv.2501.14095, and Yu, Ren and Zhou (2024) doi:10.3150/23-BEJ1706. Private score plot frames use smooth sensitivity quantiles from Nissim, Raskhodnikova and Smith (2007) doi:10.1145/1250790.1250803. Private score histograms use additive and sparse histogram ideas from Wasserman and Zhou (2010) doi:10.1198/jasa.2009.tm08651 and Karwa and Vadhan (2018) doi:10.4230/LIPIcs.ITCS.2018.44.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK7 OK2026-06-05
Documentation
- Help pages
- 15 (1 internal)includes a package overview page
- Examples that run
- 100%11 run in a plain check (1 of them under a condition)
- Exported functions with examples
- 100%
- Documented parameters
- 94%
- Return-value docs
- 100%
- Help pages citing references
- 53% with a bibliography file
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- 100%
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Checks run against github.com/yejinjo0220/dppca on 2026-10-21.
GitHub Pages site last deployed 2026-10-17
pkgdown site last built 2026-05-31, before release 0.1.0 (2026-06-04)
Dependencies
Code & Tests
Datasets
People & History
- 0.1.0Latest2026-06-04 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-04
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.3.0
- Bundled data
- 1.5 MB / 3 files
- Download size
- 3.4 MB
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