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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) <<a href="https://doi.org/10.1002%2Fsam.70053" target="_top">doi:10.1002/sam.70053</a>>. Private scree summaries use mechanisms motivated by Dwork and Roth (2014) <<a href="https://doi.org/10.1561%2F0400000042" target="_top">doi:10.1561/0400000042</a>>, Ramsay and Spicker (2025) <<a href="https://doi.org/10.48550%2FarXiv.2501.14095" target="_top">doi:10.48550/arXiv.2501.14095</a>>, and Yu, Ren and Zhou (2024) <<a href="https://doi.org/10.3150%2F23-BEJ1706" target="_top">doi:10.3150/23-BEJ1706</a>>. Private score plot frames use smooth sensitivity quantiles from Nissim, Raskhodnikova and Smith (2007) <<a href="https://doi.org/10.1145%2F1250790.1250803" target="_top">doi:10.1145/1250790.1250803</a>>. Private score histograms use additive and sparse histogram ideas from Wasserman and Zhou (2010) <<a href="https://doi.org/10.1198%2Fjasa.2009.tm08651" target="_top">doi:10.1198/jasa.2009.tm08651</a>> and Karwa and Vadhan (2018) <<a href="https://doi.org/10.4230%2FLIPIcs.ITCS.2018.44" target="_top">doi:10.4230/LIPIcs.ITCS.2018.44</a>>. https://CRAN.R-project.org/package=dppca