Package: r-cran-huge Version: 1.3.2-1 Architecture: amd64 Maintainer: Debian R Packages Maintainers Installed-Size: 1890 Depends: r-base-core (>= 3.5.2-1), r-api-3.5, r-cran-matrix, r-cran-igraph, r-cran-mass, r-cran-rcpp, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2) Filename: buster/r-cran-huge/r-cran-huge_1.3.2-1_amd64.deb Size: 1636032 MD5sum: c2448ff3bf5b63c28ae25c1430d810f1 SHA1: 6d565e2a4910f3b44d24b3057790050e49d361af SHA256: 17d636dd168207215ab7a994167174f688745c3b42f07da785fecbd01bd0502b Section: gnu-r Priority: optional Homepage: https://cran.r-project.org/package=huge Description: High-Dimensional Undirected Graph Estimation Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso.