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index.html

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index.md

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@@ -17,8 +17,8 @@ a precision matrix, making them user-friendly and useful for researchers
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and practitioners.
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\\ \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}\_{\Omega \succ 0}
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\left\\ -\log\det(\Omega) + \text{tr}(S\Omega) +
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\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) \right\\, \\ \\
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-\log\det(\Omega) + \text{tr}(S\Omega) +
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\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega), \\ \\
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\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) = \alpha
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\mathcal{P}^\text{idv}\_{\lambda,\gamma}(\Omega) + (1-\alpha)
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\mathcal{P}^\text{grp}\_{\lambda,\gamma}(\Omega), \\ \\

llms.txt

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@@ -17,8 +17,8 @@ a precision matrix, making them user-friendly and useful for researchers
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and practitioners.
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\\ \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}\_{\Omega \succ 0}
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\left\\ -\log\det(\Omega) + \text{tr}(S\Omega) +
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\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) \right\\, \\ \\
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-\log\det(\Omega) + \text{tr}(S\Omega) +
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\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega), \\ \\
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\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) = \alpha
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\mathcal{P}^\text{idv}\_{\lambda,\gamma}(\Omega) + (1-\alpha)
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\mathcal{P}^\text{grp}\_{\lambda,\gamma}(\Omega), \\ \\

pkgdown.yml

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@@ -4,7 +4,7 @@ pkgdown_sha: ~
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articles:
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crit: crit.html
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pen_est: pen_est.html
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last_built: 2026-04-19T04:03Z
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last_built: 2026-04-19T04:07Z
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urls:
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reference: https://shiying-xiao.com/grasps/reference
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article: https://shiying-xiao.com/grasps/articles

reference/grasps-2.png

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reference/grasps.html

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reference/grasps.md

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@@ -415,19 +415,19 @@ plot(prec)
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## performance
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performance(hatOmega = prec$hatOmega, Omega = sim$Omega)
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#> measure value
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#> 1 sparsity 0.7862
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#> 2 Frobenius 34.8430
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#> 3 KL 12.5488
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#> 4 quadratic 161.4326
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#> 5 spectral 19.8343
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#> 6 TP 24.0000
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#> 7 TN 318.0000
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#> 8 FP 69.0000
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#> 9 FN 24.0000
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#> 10 TPR 0.5000
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#> 11 FPR 0.1783
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#> 12 F1 0.3404
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#> 13 MCC 0.2459
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#> 1 sparsity 0.8851
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#> 2 Frobenius 23.9251
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#> 3 KL 7.6715
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#> 4 quadratic 68.7751
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#> 5 spectral 12.3687
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#> 6 TP 23.0000
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#> 7 TN 360.0000
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#> 8 FP 27.0000
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#> 9 FN 25.0000
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#> 10 TPR 0.4792
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#> 11 FPR 0.0698
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#> 12 F1 0.4694
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#> 13 MCC 0.4022
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## adjacency matrix: diagonal = 0; raw partial correlations;
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## no thresholding; weighted network

reference/performance-2.png

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reference/performance-3.png

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