make_patterns

Builds the full design grid of binary fixed-effect patterns used by COLA
to aggregate counts and outcome sums. When `intercept = TRUE`, an `Intercept`
column of ones is included and all other variables are expanded over {0,1}.

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 

make_patterns function

<dl><dt>x_names</dt>
<dd>Character vector of fixed-effect names. If `intercept = TRUE`,
it may include `"Intercept"`; otherwise it must not.</dd>
<dt>intercept</dt>
<dd>Logical; include a fixed intercept column. Default: `TRUE`.</dd></dl>

Arguments

Construct binary covariate pattern matrix — make_patterns

<dl>

<dt>x_names</dt>
<dd>Character vector of fixed-effect names. If `intercept = TRUE`,
it may include `"Intercept"`; otherwise it must not.</dd>


<dt>intercept</dt>
<dd>Logical; include a fixed intercept column. Default: `TRUE`.</dd>

</dl>

Construct binary covariate pattern matrix

make_patterns: Construct binary covariate pattern matrix

Description

Usage

Value

Arguments