pda: Privacy-Preserving Distributed Algorithms
A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, or Poisson regression for count outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.
| Version: |
1.0-2 |
| Imports: |
Rcpp (≥ 0.12.19), stats, httr, rvest, jsonlite, data.table, survival |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
imager |
| Published: |
2020-12-10 |
| Author: |
Chongliang Luo [aut, cre],
Rui Duan [aut],
Mackenzie Edmondson [aut],
Jiayi Tong [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 |
| CRAN checks: |
pda results |
Documentation:
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