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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/>.

Versions across snapshots

VersionRepositoryFileSize
1.3.0 rolling linux/jammy R-4.5 pda_1.3.0.tar.gz 882.0 KiB
1.3.0 rolling linux/noble R-4.5 pda_1.3.0.tar.gz 885.8 KiB
1.3.0 rolling source/ R- pda_1.3.0.tar.gz 360.2 KiB
1.3.0 latest linux/jammy R-4.5 pda_1.3.0.tar.gz 882.0 KiB
1.3.0 latest linux/noble R-4.5 pda_1.3.0.tar.gz 885.8 KiB
1.3.0 latest source/ R- pda_1.3.0.tar.gz 360.2 KiB
1.3.0 2026-04-26 source/ R- pda_1.3.0.tar.gz 360.2 KiB
1.3.0 2026-04-23 source/ R- pda_1.3.0.tar.gz 360.2 KiB
1.3.0 2026-04-09 windows/windows R-4.5 pda_1.3.0.zip 1.1 MiB
1.2.8 2025-04-20 source/ R- pda_1.2.8.tar.gz 198.6 KiB

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