pdaCatalog

use this function to guide end-users step-by-step to identify best pda models for their tasks, and set up control.

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 

pdaCatalog function

<dl><dt>task</dt>
<dd>user-specified task, c('Regression', 'Survival', 'Trial_emulation', 'Causal_inference', 'Design_analysis', 'Clustering'). If no specify, display all models</dd>
<dt>write_json_file_path</dt>
<dd>directory path to write the control file to</dd>
<dt>optim_maxit</dt>
<dd>option in the control file for the optimization in pda, default 100</dd>
<dt>optim_method</dt>
<dd>option in the control file for the optimization in pda, default "BFGS"</dd>
<dt>init_method</dt>
<dd>option in the control file for calculating the initial estimate in pda, default "meta"</dd></dl>

Arguments

use this function to guide end-users step-by-step to identify best pda models for their tasks, and set up control. — pdaCatalog

<dl>

<dt>task</dt>
<dd>user-specified task, c('Regression', 'Survival', 'Trial_emulation', 'Causal_inference', 'Design_analysis', 'Clustering'). If no specify, display all models</dd>


<dt>write_json_file_path</dt>
<dd>directory path to write the control file to</dd>


<dt>optim_maxit</dt>
<dd>option in the control file for the optimization in pda, default 100</dd>


<dt>optim_method</dt>
<dd>option in the control file for the optimization in pda, default "BFGS"</dd>


<dt>init_method</dt>
<dd>option in the control file for calculating the initial estimate in pda, default "meta"</dd>

</dl>

use this function to guide end-users step-by-step to identify best pda models for their tasks, and set up control.

pdaCatalog: use this function to guide end-users step-by-step to identify best pda models for their tasks, and set up control.

Description

Usage

Value

Arguments

See Also