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.

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conda 2.2 MB | linux-64/r-huge-1.2.7-r351hc070d10_2.tar.bz2  7 months and 18 days ago conda-forge 717 main cf201901
conda 2.2 MB | win-64/r-huge-1.2.7-r341h6115d3f_1.tar.bz2  8 months and 3 days ago conda-forge 300 main cf201901
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conda 2.2 MB | linux-64/r-huge-1.2.7-r341hc070d10_1.tar.bz2  8 months and 3 days ago conda-forge 675 main cf201901
conda 2.2 MB | linux-64/r-huge-1.2.7-r341hc070d10_0.tar.bz2  10 months and 29 days ago conda-forge 958 main cf201901
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