Implementation of Fused MGM to Infer 2-Class Networks
Overview
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.
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespaces in Imports field not imported from: ‘bigalgebra’ ‘biganalytics’ All declared Imports should be used.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 9
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 60%
- Documented parameters
- 97%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing fusedMGM adds 29 packages (18 with compiled code, 43 MB of source tarballs). Heaviest: BH 14 MB, stringi 11 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 0.1.2Latest
- RR 4.4.0 released · 2024-04-24
- 0.1.0.12023-04-25 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 0.1.0.02023-04-14
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-04-14
- Total releases
- 3 / 3 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 216 KB / 4 files
- Download size
- 244 KB
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