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JointNets  

End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application
View on CRAN: Click here


Download and install JointNets package within the R console
Install from CRAN:
install.packages("JointNets")

Install from Github:
library("remotes")
install_github("cran/JointNets")

Install by package version:
library("remotes")
install_version("JointNets", "2.0.1")



Attach the package and use:
library("JointNets")
Maintained by
Arshdeep Sekhon
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-12-25
Latest Update: 2023-02-02
Description:
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 doi10.1007s10994-017-5635-7 WSIMULE Singh C et al. 2017 arXiv1709.04090v2 DIFFEE Wang B et al. 2018 arXiv1710.11223 JEEK Wang B et al. 2018 arXiv1806.00548 JGLDanaher P et al. 2012 arXiv1111.0324 and kdiffnet Sekhon A et al preprint for publication.
How to cite:
Arshdeep Sekhon (2018). JointNets: End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application. R package version 2.0.1, https://cran.r-project.org/web/packages/JointNets. Accessed 21 Feb. 2025.
Previous versions and publish date:
1.0.0 (2018-12-25 23:20), 2.0.0 (2019-07-29 01:30), 2.0.1 (2019-07-30 00:40)
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Full JointNets package functions and examples
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