Deploying to gh-pages from @ a846ed0 🚀

Author: Carol-seven

index.html

@@ -16,9 +16,9 @@ that incorporate both element-wise and group-wise penalties to estimate
 a precision matrix, making them user-friendly and useful for researchers
 and practitioners.
 
-\\ \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}\_{\Omega \succ 0}
+\\ \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}\_{\Omega \succ 0} \\
 -\log\det(\Omega) + \text{tr}(S\Omega) +
-\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega), \\ \\
+\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) \\, \\ \\
 \mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) = \alpha
 \mathcal{P}^\text{idv}\_{\lambda,\gamma}(\Omega) + (1-\alpha)
 \mathcal{P}^\text{grp}\_{\lambda,\gamma}(\Omega), \\ \\

index.md

@@ -16,9 +16,9 @@ that incorporate both element-wise and group-wise penalties to estimate
 a precision matrix, making them user-friendly and useful for researchers
 and practitioners.
 
-\\ \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}\_{\Omega \succ 0}
+\\ \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}\_{\Omega \succ 0} \\
 -\log\det(\Omega) + \text{tr}(S\Omega) +
-\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega), \\ \\
+\mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) \\, \\ \\
 \mathcal{P}\_{\lambda,\alpha,\gamma}(\Omega) = \alpha
 \mathcal{P}^\text{idv}\_{\lambda,\gamma}(\Omega) + (1-\alpha)
 \mathcal{P}^\text{grp}\_{\lambda,\gamma}(\Omega), \\ \\

llms.txt

@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   crit: crit.html
   pen_est: pen_est.html
-last_built: 2026-04-19T04:07Z
+last_built: 2026-04-19T14:53Z
 urls:
   reference: https://shiying-xiao.com/grasps/reference
   article: https://shiying-xiao.com/grasps/articles

pkgdown.yml

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