论文标题
rags2ridges：用于高维精度矩阵的图形建模的一站式车间
rags2ridges: A One-Stop-Shop for Graphical Modeling of High-Dimensional Precision Matrices
论文作者
论文摘要
图形模型是代表随机变量之间条件独立性属性的无向网络。图形建模已成为多元数据的系统或网络方法的一部分，特别是当变量维度超过观测值时。 RAGS2RIDGES是用于高维精度矩阵的图形建模的R软件包。它为从高维数据中的高斯图形模型进行提取，可视化和分析提供了模块化框架。此外，它可以处理先前信息的合并以及多个异质数据类。因此，它为高维精度矩阵的图形建模提供了一站式车展。包装的功能通过与阿尔茨海默氏病患者的血液代谢物测量有关的示例数据集进行了说明。
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.
