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>.
Version: | 0.1.0.1 |
Depends: | R (≥ 2.10) |
Imports: | fastDummies, parallel, bigmemory, gplots |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2023-04-25 |
DOI: | 10.32614/CRAN.package.fusedMGM |
Author: | Jaehyun Park [aut, cre, cph], Sungho Won [ths] |
Maintainer: | Jaehyun Park <J.31.Park at gmail.com> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | fusedMGM results |
Reference manual: | fusedMGM.pdf |
Package source: | fusedMGM_0.1.0.1.tar.gz |
Windows binaries: | r-devel: fusedMGM_0.1.0.1.zip, r-release: fusedMGM_0.1.0.1.zip, r-oldrel: fusedMGM_0.1.0.1.zip |
macOS binaries: | r-release (arm64): fusedMGM_0.1.0.1.tgz, r-oldrel (arm64): fusedMGM_0.1.0.1.tgz, r-release (x86_64): fusedMGM_0.1.0.1.tgz, r-oldrel (x86_64): fusedMGM_0.1.0.1.tgz |
Old sources: | fusedMGM archive |
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