Package: pda 1.2.7

Jiajie Chen

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, Poisson regression for count outcome, or multi-categorical regression for nominal or ordinal outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online 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/>.

Authors:Chongliang Luo [aut], Rui Duan [aut], Mackenzie Edmondson [aut], Jiayi Tong [aut], Xiaokang Liu [aut], Kenneth Locke [aut], Jiajie Chen [cre], Yong Chen [aut], Penn Computing Inference Learning lab [cph]

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6 exports 0.49 score 47 dependencies 1 mentions 9 scripts 262 downloads

Last updated 7 months agofrom:5027fce825. Checks:OK: 2. Indexed: yes.

Exports:getCloudConfigpdapdaGetpdaListpdaPutpdaSync

Dependencies:askpassclicodetoolscurldata.tablefansiforeachglmnetgluehttriteratorsjsonlitelatticelifecyclemagrittrMASSmathjaxrMatrixmetadatmetaformimeminqanlmenumDerivopensslordinalpbapplypillarpkgconfigplyrR6RcppRcppArmadilloRcppEigenrlangrvestselectrshapestringistringrsurvivalsystibbleucminfutf8vctrsxml2