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A graphical model is an undirected network representing the conditionalindependence properties between random variables. Graphical modeling has becomepart and parcel of systems or network approaches to multivariate data, inparticular when the variable dimension exceeds the observation dimension.rags2ridges is an R package for graphical modeling of high-dimensionalprecision matrices. It provides a modular framework for the extraction,visualization, and analysis of Gaussian graphical models from high-dimensionaldata. Moreover, it can handle the incorporation of prior information as well asmultiple heterogeneous data classes. As such, it provides a one-stop-shop forgraphical modeling of high-dimensional precision matrices. The functionality ofthe package is illustrated with an example dataset pertaining to blood-basedmetabolite measurements in persons suffering from Alzheimer's Disease.

rags2ridges: A One-Stop-Shop for Graphical Modeling of High-Dimensional Precision Matrices