update README; local::.

Author: Carol-seven

.github/workflows/R-CMD-check.yaml

@@ -42,7 +42,7 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::rcmdcheck
+          extra-packages: any::rcmdcheck, local::.
           needs: check
 
       - uses: r-lib/actions/check-r-package@v2

README.Rmd

@@ -38,18 +38,20 @@ matrix, making them user-friendly and useful for researchers and practitioners.
 
 $$
 \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}_{\Omega \succ 0}
-\{ -\log\det(\Omega) + \text{tr}(S\Omega) + \lambda P_{\alpha,\gamma}(\Omega) \},
+\left\{ -\log\det(\Omega) + \text{tr}(S\Omega)
++ \mathcal{P}_{\lambda,\alpha,\gamma}(\Omega) \right\},
 $$
 $$
-P_{\alpha,\gamma}(\Omega)
-= \alpha P^\text{idv}_\gamma(\Omega) + (1-\alpha) P^\text{grp}_\gamma(\Omega),
+\mathcal{P}_{\lambda,\alpha,\gamma}(\Omega)
+= \alpha \mathcal{P}^\text{idv}_{\lambda,\gamma}(\Omega) + (1-\alpha) \mathcal{P}^\text{grp}_{\lambda,\gamma}(\Omega),
 $$
 $$
-P^\text{idv}_\gamma(\Omega) = \sum_{i,j} p_\gamma(\vert\omega_{ij}\vert),
+\mathcal{P}^\text{idv}_{\lambda,\gamma}(\Omega)
+= \sum_{i,j} P_{\lambda,\gamma}(\lvert\omega_{ij}\rvert),
 $$
 $$
-P^\text{grp}_\gamma(\Omega)
-= \sum_{g,g^\prime} p_\gamma(\Vert\Omega_{gg^\prime}\Vert_F).
+\mathcal{P}^\text{grp}_{\lambda,\gamma}(\Omega)
+= \sum_{g,g^\prime} P_{\lambda,\gamma}(\lVert\Omega_{gg^\prime}\rVert_F).
 $$
 
 
@@ -116,7 +118,7 @@ library(grasps)
 set.seed(1234)
 
 ## block-structured precision matrix based on SBM
-sim <- gen_prec_sbm(d = 30, K = 3,
+sim <- gen_prec_sbm(p = 30, K = 3,
                     within.prob = 0.25, between.prob = 0.05,
                     weight.dists = list("gamma", "unif"),
                     weight.paras = list(c(shape = 20, rate = 10),

README.md

@@ -15,16 +15,23 @@ The goal of **grasps** is to provide a collection of statistical methods that
 incorporate both element-wise and group-wise penalties to estimate a precision
 matrix, making them user-friendly and useful for researchers and practitioners.
 
-$$\hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}_{\Omega \succ 0}
-\{ -\log\det(\Omega) + \text{tr}(S\Omega) + \lambda P_{\alpha,\gamma}(\Omega) \},$$
-
-$$P_{\alpha,\gamma}(\Omega)
-= \alpha P^\text{idv}_\gamma(\Omega) + (1-\alpha) P^\text{grp}_\gamma(\Omega),$$
-
-$$P^\text{idv}_\gamma(\Omega) = \sum_{i,j} p_\gamma(\vert\omega_{ij}\vert),$$
-
-$$P^\text{grp}_\gamma(\Omega)
-= \sum_{g,g^\prime} p_\gamma(\Vert\Omega_{gg^\prime}\Vert_F).$$
+$$
+\hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}_{\Omega \succ 0}
+\left\{ -\log\det(\Omega) + \text{tr}(S\Omega)
++ \mathcal{P}_{\lambda,\alpha,\gamma}(\Omega) \right\},
+$$
+$$
+\mathcal{P}_{\lambda,\alpha,\gamma}(\Omega)
+= \alpha \mathcal{P}^\text{idv}_{\lambda,\gamma}(\Omega) + (1-\alpha) \mathcal{P}^\text{grp}_{\lambda,\gamma}(\Omega),
+$$
+$$
+\mathcal{P}^\text{idv}_{\lambda,\gamma}(\Omega)
+= \sum_{i,j} P_{\lambda,\gamma}(\lvert\omega_{ij}\rvert),
+$$
+$$
+\mathcal{P}^\text{grp}_{\lambda,\gamma}(\Omega)
+= \sum_{g,g^\prime} P_{\lambda,\gamma}(\lVert\Omega_{gg^\prime}\rVert_F).
+$$
 
 For more details, see the vignette
 [Penalized Precision Matrix Estimation in grasps](https://shiying-xiao.com/grasps/articles/pen_est#sparse-group-estimator).
@@ -77,7 +84,7 @@ library(grasps)
 set.seed(1234)
 
 ## block-structured precision matrix based on SBM
-sim <- gen_prec_sbm(d = 30, K = 3,
+sim <- gen_prec_sbm(p = 30, K = 3,
                     within.prob = 0.25, between.prob = 0.05,
                     weight.dists = list("gamma", "unif"),
                     weight.paras = list(c(shape = 20, rate = 10),
@@ -95,18 +102,18 @@ res <- grasps(X = X, membership = sim$membership, penalty = "adapt", crit = "HBI
 plot(res)
 ```
 
-<img src="man/figures/README-unnamed-chunk-2-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-2-1.png" alt="" width="100%" />
 
 ``` r
 
 ## performance
 performance(hatOmega = res$hatOmega, Omega = sim$Omega)
 #>      measure    value
 #> 1   sparsity   0.9103
-#> 2  Frobenius  24.6796
-#> 3         KL   7.2063
-#> 4  quadratic  54.1949
-#> 5   spectral  13.1336
+#> 2  Frobenius  24.6557
+#> 3         KL   7.2100
+#> 4  quadratic  54.4284
+#> 5   spectral  13.1234
 #> 6         TP  22.0000
 #> 7         TN 370.0000
 #> 8         FP  17.0000