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/>.
| Version: |
1.3.0 |
| 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 |
| Suggests: |
lme4 |
| Published: |
2025-11-17 |
| DOI: |
10.32614/CRAN.package.pda |
| 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] |
| Maintainer: |
Chongliang Luo <luocl3009 at gmail.com> |
| License: |
Apache License 2.0 |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
pda results |