Implementation of fused Markov graphical model (FMGM; Park and Won, 2022). The functions include building mixed graphical model (MGM) objects from data, inference of networks using FMGM, stable edge-specific penalty selection (StEPS) for the determination of penalization parameters, and the visualization. For details, please refer to Park and Won (2022) <doi:10.48550/arXiv.2208.14959>.

Jaehyun Park

fusedMGM

Implementation of Fused MGM to Infer 2-Class Networks

Sungho Won

MGM function

<dl><dt>X</dt>
<dd>data frame or matrix of continuous variables (row: observation, column: variable)</dd>
<dt>Y</dt>
<dd>data frame or matrix of discrete variables (row: observation, column: variable)</dd>
<dt>g</dt>
<dd>group index, needed for temporary files</dd></dl>

Arguments

Defining S3 object "MGM" — MGM

<dl>

<dt>X</dt>
<dd>data frame or matrix of continuous variables (row: observation, column: variable)</dd>


<dt>Y</dt>
<dd>data frame or matrix of discrete variables (row: observation, column: variable)</dd>


<dt>g</dt>
<dd>group index, needed for temporary files</dd>

</dl>

MGM: Defining S3 object "MGM"

Description

Usage

Value

Arguments