Package: pda
Type: Package
Title: Privacy-Preserving Distributed Algorithms
Version: 1.0-2
Date: 2020-12-10
Authors@R: c(person("Chongliang", "Luo", role=c("aut","cre"), email ="luocl3009@gmail.com"),
                person("Rui", "Duan", role="aut"), person("Mackenzie", "Edmondson", role="aut"),
                person("Jiayi", "Tong", role="aut"),
                person("Yong", "Chen", role="aut", email ="ychen123@upenn.edu"),
                person("Penn Computing Inference Learning (PennCIL) lab", role = c("cph")))
Description: 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/>.
Maintainer: Chongliang Luo <luocl3009@gmail.com>
License: Apache License 2.0
Suggests: imager
Imports: Rcpp (>= 0.12.19), stats, httr, rvest, jsonlite, data.table,
        survival
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.1
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-12-10 20:53:23 UTC; chl18019
Author: Chongliang Luo [aut, cre],
  Rui Duan [aut],
  Mackenzie Edmondson [aut],
  Jiayi Tong [aut],
  Yong Chen [aut],
  Penn Computing Inference Learning (PennCIL) lab [cph]
Repository: CRAN
Date/Publication: 2020-12-10 21:20:02 UTC
Built: R 4.0.2; x86_64-apple-darwin17.0; 2020-12-11 11:53:57 UTC; unix
Archs: pda.so.dSYM
