Package: dppca 0.1.0
dppca: Differentially Private Principal Component Analysis Visualization
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>.
Authors:
dppca_0.1.0.tar.gz
dppca_0.1.0.zip(r-4.7)dppca_0.1.0.zip(r-4.6)dppca_0.1.0.zip(r-4.5)
dppca_0.1.0.tgz(r-4.6-any)dppca_0.1.0.tgz(r-4.5-any)
dppca_0.1.0.tar.gz(r-4.7-any)dppca_0.1.0.tar.gz(r-4.6-any)
dppca_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
dppca/json (API)
| # Install 'dppca' in R: |
| install.packages('dppca', repos = c('https://yejinjo0220.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yejinjo0220/dppca/issues
Pkgdown/docs site:https://yejinjo0220.github.io
Last updated from:e5fc913fd1. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 137 | ||
| source / vignettes | OK | 176 | ||
| linux-release-x86_64 | OK | 133 | ||
| macos-release-arm64 | OK | 192 | ||
| macos-oldrel-arm64 | OK | 178 | ||
| windows-devel | OK | 117 | ||
| windows-release | OK | 102 | ||
| windows-oldrel | OK | 90 | ||
| wasm-release | OK | 118 |
Exports:clipped_controldp_pc_dirdp_scoredp_score_groupdp_score_plotdp_score_plot_groupdp_screedp_scree_plotdppca_apphuber_controlpmwm_control
Dependencies:clicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMatrixpatchworkpillarpkgconfigR6rARPACKrbibutilsRColorBrewerRcppRcppEigenRdpackrlangRSpectraS7scalestibbletidyselectutf8vctrsVGAMviridisLitewithr
Algorithms
Rendered fromalgorithms.Rmdusingknitr::rmarkdownon Jun 05 2026.Last update: 2026-05-22
Started: 2026-05-05
DP score plots in dppca
Rendered fromdp_score.Rmdusingknitr::rmarkdownon Jun 05 2026.Last update: 2026-05-31
Started: 2026-05-05
DP scree in dppca
Rendered fromdp_scree.Rmdusingknitr::rmarkdownon Jun 05 2026.Last update: 2026-05-07
Started: 2026-05-05
PC Directions in dppca
Rendered frompc_direction.Rmdusingknitr::rmarkdownon Jun 05 2026.Last update: 2026-05-20
Started: 2026-05-05
Principal Component Analysis Background
Rendered frompca_background.Rmdusingknitr::rmarkdownon Jun 05 2026.Last update: 2026-05-05
Started: 2026-05-05
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Adult numeric data | adult |
| Control options for clipped scree estimation | clipped_control |
| Estimate principal component directions | dp_pc_dir |
| Differentially private score histograms | dp_score |
| Group-wise differentially private score histograms | dp_score_group |
| Plot differentially private score histograms | dp_score_plot |
| Plot group-wise differentially private score histograms | dp_score_plot_group |
| Differentially private scree values | dp_scree |
| Plot differentially private scree estimates | dp_scree_plot |
| Launch the dppca Shiny app | dppca_app |
| Five Gaussian clusters | gau |
| Five Gaussian clusters with group labels | gau_g |
| Control options for Huber scree estimation | huber_control |
| Control options for private modified winsorized scree estimation | pmwm_control |
