ODACH_CC.derive

internal

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/>.

Chongliang Luo

Privacy-Preserving Distributed Algorithms

Rui Duan

Mackenzie Edmondson

Jiayi Tong

Xiaokang Liu

Kenneth Locke

Jie Hu

Bingyu Zhang

Yicheng Shen

Yudong Wang

Yiwen Lu

Lu Li

Yong Chen

Penn Computing Inference Learning (PennCIL) lab 

ODACH_CC.derive function

<dl><dt>ipdata</dt>
<dd>individual participant data</dd>
<dt>control</dt>
<dd>pda control data</dd>
<dt>config</dt>
<dd>local site configuration</dd></dl>

Arguments

Generate pda derivatives — ODACH_CC.derive

<dl>

<dt>ipdata</dt>
<dd>individual participant data</dd>


<dt>control</dt>
<dd>pda control data</dd>


<dt>config</dt>
<dd>local site configuration</dd>

</dl>

Generate pda derivatives

ODACH_CC.derive: Generate pda derivatives

Description

Usage

Value

Arguments

Details