Package: pda 1.3.0

pda: Privacy-Preserving Distributed Algorithms

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/>.

Authors: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 lab [cph]