rags2ridges: A One-Stop-Shop for Graphical Modeling of High-Dimensional Precision Matrices
This provides a tool for researchers in fields like bioinformatics and statistics dealing with high-dimensional data, but it is incremental as it packages existing methods into a unified software solution.
The authors tackled the problem of graphical modeling for high-dimensional precision matrices by developing rags2ridges, an R package that provides a modular framework for extraction, visualization, and analysis, including handling prior information and multiple data classes, as demonstrated with an Alzheimer's Disease metabolite dataset.
A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer's Disease.