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, or Poisson regression for count outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. For more information, please visit our software websites: < https://github.com/Penncil/pda>, and < https://pdamethods.org/>.


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Reference manual

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install.packages("pda")

1.0-2 by Chongliang Luo, a month ago


Browse source code at https://github.com/cran/pda


Authors: Chongliang Luo [aut, cre] , Rui Duan [aut] , Mackenzie Edmondson [aut] , Jiayi Tong [aut] , Yong Chen [aut] , Penn Computing Inference Learning (PennCIL) lab [cph]


Documentation:   PDF Manual  


Apache License 2.0 license


Imports Rcpp, stats, httr, rvest, jsonlite, data.table, survival

Suggests imager

Linking to Rcpp, RcppArmadillo


See at CRAN