pda: Privacy-Preserving Distributed Algorithms

A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, Poisson regression for count outcome, or multi-categorical regression for nominal or ordinal outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

Getting started

Package details

AuthorChongliang Luo [aut], Rui Duan [aut], Mackenzie Edmondson [aut], Jiayi Tong [aut], Xiaokang Liu [aut], Kenneth Locke [aut], Yiwen Lu [cre], Yong Chen [aut], Penn Computing Inference Learning (PennCIL) lab [cph]
MaintainerYiwen Lu <yiwenlu@sas.upenn.edu>
LicenseApache License 2.0
Version1.2.8
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("pda")

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pda documentation built on April 3, 2025, 10:28 p.m.