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  "Description": "Fits sparse partial correlation networks for\nmatrix-variate data by extending the SPACE joint partial\ncorrelation estimation framework to a Kronecker-product\ncovariance structure. All partial correlations are estimated\nsimultaneously via an L1-penalized (lasso) shooting algorithm\nwithin a single optimization framework, which preserves\nsymmetry of the estimated network and avoids the\ntuning-parameter selection difficulties of separate node-wise\nregressions. Optional features include column reweighting,\nresidual variance re-estimation across outer iterations, and\nautomatic generation of a lasso penalty sequence for tuning.",
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