pda

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/).

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