Package: pda
Type: Package
Title: Privacy-Preserving Distributed Algorithms
Version: 1.3.0
Date: 2025-11-11
Authors@R: c(person("Chongliang", "Luo", role=c("cre"), 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("Jie", "Hu", role="aut"),
                person("Bingyu", "Zhang", role="aut"),
                person("Yicheng", "Shen", role="aut"), 
                person("Yudong", "Wang", role="aut"),
                person("Yiwen", "Lu", role="aut"), 
                person("Lu", "Li", 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 (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer 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: Chongliang Luo <luocl3009@gmail.com>
License: Apache License 2.0
Suggests: lme4
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 0.12.19), stats, httr, rvest, jsonlite, data.table,
        cobalt, EmpiricalCalibration, survival, minqa, glmnet, MASS,
        numDeriv, metafor, Matrix, ordinal, plyr, tidyr, tibble, dplyr,
        geex, data.tree
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
RoxygenNote: 7.3.3
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Packaged: 2025-11-16 17:36:26 UTC; chongliang
Author: Chongliang Luo [cre],
  Rui Duan [aut],
  Mackenzie Edmondson [aut],
  Jiayi Tong [aut],
  Xiaokang Liu [aut],
  Kenneth Locke [aut],
  Jie Hu [aut],
  Bingyu Zhang [aut],
  Yicheng Shen [aut],
  Yudong Wang [aut],
  Yiwen Lu [aut],
  Lu Li [aut],
  Yong Chen [aut],
  Penn Computing Inference Learning (PennCIL) lab [cph]
Repository: CRAN
Date/Publication: 2025-11-17 21:50:52 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2025-11-26 04:58:52 UTC; windows
Archs: x64
