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, 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/>.

Version: 1.0-2
Imports: Rcpp (≥ 0.12.19), stats, httr, rvest, jsonlite, data.table, survival
LinkingTo: Rcpp, RcppArmadillo
Suggests: imager
Published: 2020-12-10
Author: Chongliang Luo [aut, cre], Rui Duan [aut], Mackenzie Edmondson [aut], Jiayi Tong [aut], Yong Chen [aut], Penn Computing Inference Learning (PennCIL) lab [cph]
Maintainer: Chongliang Luo <luocl3009 at gmail.com>
License: Apache License 2.0
NeedsCompilation: yes
CRAN checks: pda results

Downloads:

Reference manual: pda.pdf
Package source: pda_1.0-2.tar.gz
Windows binaries: r-devel: pda_1.0-2.zip, r-devel-UCRT: pda_1.0-2.zip, r-release: pda_1.0-2.zip, r-oldrel: pda_1.0-2.zip
macOS binaries: r-release (arm64): pda_1.0-2.tgz, r-release (x86_64): pda_1.0-2.tgz, r-oldrel: pda_1.0-2.tgz
Old sources: pda archive

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