Joint Generalized Estimating Equation Solver
Fits two different joint generalized estimating equation models to multivariate longitudinal data.

Performs the Joint Graphical Lasso for sparse inverse covariance
estimation on multiple classes
The Joint Graphical Lasso is a generalized method for
estimating Gaussian graphical models/ sparse inverse covariance
matrices/ biological networks on multiple classes of data.  We
solve JGL under two penalty functions: The Fused Graphical
Lasso (FGL), which employs a fused penalty to encourage inverse
covariance matrices to be similar across classes, and the Group
Graphical Lasso (GGL), which encourages similar network
structure between classes.  FGL is recommended over GGL for
most applications.

Performs the Joint Graphical Lasso for Sparse Inverse Covariance
Estimation on Multiple Classes
The Joint Graphical Lasso is a generalized method for
estimating Gaussian graphical models/ sparse inverse covariance
matrices/ biological networks on multiple classes of data.  We
solve JGL under two penalty functions: The Fused Graphical
Lasso (FGL), which employs a fused penalty to encourage inverse
covariance matrices to be similar across classes, and the Group
Graphical Lasso (GGL), which encourages similar network
structure between classes.  FGL is recommended over GGL for
most applications. Reference: Danaher P, Wang P, Witten DM. (2013)
<doi:10.1111/rssb.12033>.

full functionality for JGR and additional features as well as JGR (the Java application) itself, additionally you need the start applications depending on your OS from JGR's website.

For full functionality JGR requires a start application which depends on your OS. This can be downloaded from JGR's website.

Markus Helbig <info@markushelbig.de>, Simon Urbanek
Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS. This can be downloaded from JGR's website: http://www.rosuda.org/JGR