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) ), WSIMULE (Singh C et al. (2017) ), DIFFEE (Wang B et al. (2018) ), FASJEM (Wang B et al. (2018) ), JEEK (Wang B et al. (2018) ) and DIFFEEK (Wang B et al, under final review for publication).


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("JointNets")

1.0.0 by Zhaoyang Wang, 4 months ago


https://github.com/QData/JointNets


Report a bug at https://github.com/QData/JointNets


Browse source code at https://github.com/cran/JointNets


Authors: Beilun Wang [aut] , Yanjun Qi [aut] , Zhaoyang Wang [aut, cre]


Documentation:   PDF Manual  


GPL-2 license


Imports MASS, brainR, misc3d, oro.nifti, shiny, rgl, methods

Depends on lpSolve, pcaPP, igraph, parallel


See at CRAN