Package: pda
Type: Package
Title: Privacy-Preserving Distributed Algorithms
Version: 1.2.6
Date: 2023-03-06
Authors@R: c(person("Chongliang", "Luo", role="aut", email ="luocl3009@gmail.com"),
                person("Rui", "Duan", role="aut"), person("Mackenzie", "Edmondson", role="aut"),
                person("Jiayi", "Tong", role="aut"), person("Xiaokang", "Liu", role="aut"),
                person("Kenneth", "Locke", role="aut"),
                person("Jiajie", "Chen", role="cre",email ="jiajie.chen@pennmedicine.upenn.edu"),
                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, 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/>.
Maintainer: Jiajie Chen <jiajie.chen@pennmedicine.upenn.edu>
License: Apache License 2.0
Suggests: imager, lme4
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 0.12.19), stats, httr, rvest, jsonlite, data.table,
        survival, minqa, glmnet, MASS, numDeriv, metafor, ordinal, plyr
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.2.2
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Packaged: 2023-03-08 15:40:41 UTC; thech
Author: Chongliang Luo [aut],
  Rui Duan [aut],
  Mackenzie Edmondson [aut],
  Jiayi Tong [aut],
  Xiaokang Liu [aut],
  Kenneth Locke [aut],
  Jiajie Chen [cre],
  Yong Chen [aut],
  Penn Computing Inference Learning (PennCIL) lab [cph]
Repository: CRAN
Date/Publication: 2023-03-08 16:00:02 UTC
