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  "Description": "Provides a method of recovering the precision matrix for\nGaussian graphical models efficiently. Our approach could be\ndivided into three categories. First of all, we use Hard\nGraphical Thresholding for best subset selection problem of\nGaussian graphical model, and the core concept of this method\nwas proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly,\na closed form solution for graphical lasso under acyclic graph\nstructure is implemented in our package (Fattahi and Sojoudi\n(2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore,\nwe implement block coordinate descent algorithm to efficiently\nsolve the covariance selection problem (Dempster (1972)\n<doi:10.2307/2528966>). Our package is computationally\nefficient and can solve ultra-high-dimensional problems, e.g. p\n> 10,000, in a few minutes.",
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