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  "Title": "Statistical Inference of Large-Scale Gaussian Graphical Model in\nGene Networks",
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  "Author": "Rong Zhang, Zhao Ren and Wei Chen",
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  "Description": "Provides a general framework to perform statistical\ninference of each gene pair and global inference of whole-scale\ngene pairs in gene networks using the well known Gaussian\ngraphical model (GGM) in a time-efficient manner. We focus on\nthe high-dimensional settings where p (the number of genes) is\nallowed to be far larger than n (the number of subjects). Four\nmain approaches are supported in this package: (1) the\nbivariate nodewise scaled Lasso (Ren et al (2015)\n<doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled\nLasso (Jankova and van de Geer (2017)\n<doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified\ngraphical Lasso (Jankova and van de Geer (2015)\n<doi:10.1214/15-EJS1031>) (4) the GGM estimation with false\ndiscovery rate control (FDR) using scaled Lasso or Lasso (Liu\n(2013) <doi:10.1214/13-AOS1169>). Windows users should install\n'Rtools' before the installation of this package.",
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