Privacy-Preserving Distributed Algorithms R package pda version 1.3.0 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/>. Software Depends: R (>= 4.1.0) Imports: Rcpp (>= 0.12.19), stats, httr, rvest, jsonlite, data.table, cobalt, EmpiricalCalibration, survival, minqa, glmnet, MASS, numDeriv, metafor, Matrix, ordinal, plyr, tidyr, tibble, dplyr, geex, data.tree LinkingTo: Rcpp, RcppArmadillo, RcppEigen Suggests: lme4 Chongliang Luo <luocl3009@gmail.com> Comprehensive R Archive Network (CRAN) Chongliang Luo [cre], Rui Duan [aut], Mackenzie Edmondson [aut], Jiayi Tong [aut], Xiaokang Liu [aut], Kenneth Locke [aut], Jie Hu [aut], Bingyu Zhang [aut], Yicheng Shen [aut], Yudong Wang [aut], Yiwen Lu [aut], Lu Li [aut], Yong Chen [aut], Penn Computing Inference Learning (PennCIL) lab [cph] Apache License 2.0 2025-11-17 application/tgz https://CRAN.R-project.org/package=pda doi:10.32614/CRAN.package.pda