JointNets: End-to-End Sparse Gaussian Graphical Model Simulation,
Estimation, Visualization, Evaluation and Application
An end-to-end package for learning multiple sparse Gaussian graphical models and nonparanormal models from Heterogeneous Data with Additional Knowledge. It is able to simulate multiple related graphs as well as produce samples drawn from them. Multiple state-of-the-art sparse Gaussian graphical model estimators are included to both multiple and difference estimation. Graph visualization is available in 2D as well as 3D, designed specifically for brain. Moreover, a set of evaluation metrics are integrated for easy exploration with model validity. Finally, classification using graphical model is achieved with Quadratic Discriminant Analysis. The package comes with multiple demos with datasets from various fields. Methods references: 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>), JEEK (Wang B et al. (2018) <arXiv:1806.00548>), JGL(Danaher P et al. (2012) <arXiv:1111.0324>) and kdiffnet (Sekhon A et al, preprint for publication).
||R (≥ 3.4.4), lpSolve, pcaPP, igraph, parallel, JGL
||MASS, brainR, misc3d, oro.nifti, shiny, rgl, methods
||Zhaoyang Wang [aut],
Beilun Wang [aut],
Arshdeep Sekhon [aut, cre],
Yanjun Qi [aut]
||Arshdeep Sekhon <as5cu at virginia.edu>
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