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, 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/>.
| Version: |
1.2.8 |
| 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 |
| Suggests: |
imager, lme4 |
| Published: |
2025-03-10 |
| DOI: |
10.32614/CRAN.package.pda |
| Author: |
Chongliang Luo [aut],
Rui Duan [aut],
Mackenzie Edmondson [aut],
Jiayi Tong [aut],
Xiaokang Liu [aut],
Kenneth Locke [aut],
Yiwen Lu [cre],
Yong Chen [aut],
Penn Computing Inference Learning (PennCIL) lab [cph] |
| Maintainer: |
Yiwen Lu <yiwenlu at sas.upenn.edu> |
| License: |
Apache License 2.0 |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
pda results |
Documentation:
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