Privacy-Preserving Distributed Algorithms
Overview
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/.
- Add pdaCataog() and DisC2o, ODACH_CC, COLA, ODACT, LATTE models
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From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 84 (63 internal)
- Examples that run
- 0%2 hold no code
- Exported functions with examples
- 0%
- Documented parameters
- 97%
- Return-value docs
- 100%
- Help pages citing references
- 17%
Also includes 14 demo scripts (run with demo())
Downloads
Dependencies
Installing pda adds 69 packages (37 with compiled code, 71 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Previously maintained by Yiwen Lu (1.2.8) and Jiajie Chen (1.2.5 to 1.2.7).
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- RR 4.6.0 released · 2026-04-24
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- RR 4.5.0 released · 2025-04-11
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- RR 4.4.0 released · 2024-04-24
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- RR 4.3.0 released · 2023-04-21
- 1.2.62023-03-08 · diff ↗
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- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.0-22020-12-10 · diff ↗
- 1.02020-11-23
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2020-11-23
- Total releases
- 7 / 6 yrs
- License
- Apache License 2.0 OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 235 KB / 13 files
- Download size
- 361 KB
Cite
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