dppca - Differentially Private Principal Component Analysis and Visualization

Provides tools for differentially private principal component analysis (PCA) and visualization. Includes private principal component directions, scree and proportion of variance explained (PVE) estimates, and two-dimensional private score histograms with synthetic sampled points. Supports additive and sparse histogram mechanisms, group-wise score visualizations, and optional non-private comparisons. An integrated workflow produces loading plots, scree plots, score plots, variable plots, and biplots from shared private PCA estimates. An interactive 'shiny' application is also provided. Private direction estimation is based on Kim and Jung (2025) <doi:10.1002/sam.70053>. Private scree estimation draws on 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 frames use unbounded quantile mechanisms from Durfee (2023) <doi:10.52202/075280-3394>. Private score histograms use methods related to Wasserman and Zhou (2010) <doi:10.1198/jasa.2009.tm08651> and Karwa and Vadhan (2018) <doi:10.4230/LIPIcs.ITCS.2018.44>.