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 |
| Reference manual: | pda.pdf |
| Package source: | pda_1.0-2.tar.gz |
| Windows binaries: | r-devel: pda_1.0-2.zip, r-release: pda_1.0-2.zip, r-oldrel: pda_1.0-2.zip |
| macOS binaries: | r-release: pda_1.0-2.tgz, r-oldrel: pda_1.0-2.tgz |
| Old sources: | pda archive |
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