HUGE: High-dimensional Undirected Graph Estimation

by Tour for R Project for Statistical Computing

Modern data acquisition routinely produces massive amount of complex datasets. Despite the high dimensionality and complexity, many problems have hidden structure that makes efficient statistical inference possible. One important hidden structure is sparse conditional independence graphs (or undirected graphical models). Our HUGE project aims at providing a fast and scalable toolkit for nonparametric graphical models in ultrahigh-dimensional data analysis.