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cff-version: 1.2.0
message: 'To cite package "dppca" in publications use:'
type: software
license: MIT
title: 'dppca: Differentially Private Principal Component Analysis Visualization'
version: 0.1.0
abstract: 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)
  <https://doi.org/10.1002/sam.70053>. Private scree summaries use mechanisms motivated
  by Dwork and Roth (2014) <https://doi.org/10.1561/0400000042>, Ramsay and Spicker
  (2025) <https://doi.org/10.48550/arXiv.2501.14095>, and Yu, Ren and Zhou (2024)
  <https://doi.org/10.3150/23-BEJ1706>. Private score plot frames use smooth sensitivity
  quantiles from Nissim, Raskhodnikova and Smith (2007) <https://doi.org/10.1145/1250790.1250803>.
  Private score histograms use additive and sparse histogram ideas from Wasserman
  and Zhou (2010) <https://doi.org/10.1198/jasa.2009.tm08651> and Karwa and Vadhan
  (2018) <https://doi.org/10.4230/LIPIcs.ITCS.2018.44>.
authors:
- family-names: Jo
  given-names: Yejin
  email: yejinjo0220@gmail.com
- family-names: Kim
  given-names: Minwoo
  email: mwkim.stat@gmail.com
repository: https://yejinjo0220.r-universe.dev
repository-code: https://github.com/yejinjo0220/dppca
commit: e5fc913fd11ae0906c1ca89dc797fb46a9fdc567
url: https://yejinjo0220.github.io/dppca/
date-released: '2026-05-31'
contact:
- family-names: Jo
  given-names: Yejin
  email: yejinjo0220@gmail.com