JointNets: Sparse Gaussian Graphical Model Estimation, Visualization and Evaluation

A set of tools for performing sparse Gaussian graphical model (joint, multiple and difference) estimation from high dimensional dataset. It contains a general purpose visualization function as well as a specialized function for 3d brain network. Simulation and evaluation modules are available. It also contains a simple GUI built in shiny for easy graph visualization. Methods include SIMULE (Wang B et al. (2017) <doi:10.1007/s10994-017-5635-7>), WSIMULE (Singh C et al. (2017) <arXiv:1709.04090v2>), DIFFEE (Wang B et al. (2018) <arXiv:1710.11223>), FASJEM (Wang B et al. (2018) <arXiv:1702.02715v3>), JEEK (Wang B et al. (2018) <arXiv:1806.00548>) and DIFFEEK (Wang B et al, under final review for publication).

Version: 1.0.0
Depends: R (≥ 3.0.0), lpSolve, pcaPP, igraph, parallel
Imports: MASS, brainR, misc3d, oro.nifti, shiny, rgl, methods
Published: 2018-12-25
Author: Beilun Wang [aut], Yanjun Qi [aut], Zhaoyang Wang [aut, cre]
Maintainer: Zhaoyang Wang <zw4dn at>
License: GPL-2
NeedsCompilation: no
CRAN checks: JointNets results


Reference manual: JointNets.pdf
Package source: JointNets_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: JointNets_1.0.0.tgz, r-oldrel: JointNets_1.0.0.tgz


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