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  "Title": "High-Dimensional Undirected Graph Estimation",
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  "Description": "Provides a general framework for high-dimensional\nundirected graph estimation. It integrates data preprocessing,\nneighborhood screening, graph estimation, and model selection\ntechniques into a pipeline. In preprocessing stage, the\nnonparanormal(npn) transformation is applied to help relax the\nnormality assumption. In the graph estimation stage, the graph\nstructure is estimated by Meinshausen-Buhlmann graph\nestimation, the graphical lasso, or the TIGER\n(tuning-insensitive graph estimation and regression) method,\nand the first two can be further accelerated by the lossy\nscreening rule preselecting the neighborhood of each variable\nby correlation thresholding. We target on high-dimensional data\nanalysis usually d >> n, and the computation is\nmemory-optimized using the sparse matrix output. We also\nprovide a computationally efficient approach, correlation\nthresholding graph estimation. Three\nregularization/thresholding parameter selection methods are\nincluded in this package: (1)stability approach for\nregularization selection (2) rotation information criterion (3)\nextended Bayesian information criterion which is only available\nfor the graphical lasso.",
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