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)
This package runs the Joint Graphical Lasso (JGL) method for estimating sparse inverse covariance matrices across multiple similar datasets.
Danaher P, Wang P, Witten DM. The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2014 Mar 1;76(2):373-97.
Install the package from CRAN with install.packages("JGL")
This quick 2018 update makes this old package (est. 2012) compatible with current CRAN requirements. No changes to the package's core functions have been made.