Deploying to gh-pages from @ 7d05592 🚀

Author: Carol-seven

llms.txt

@@ -151,6 +151,9 @@ of the American Statistical Association* 101 (476): 1418–29.
   Function Computation
 - [`performance()`](https://shiying-xiao.com/grasps/reference/performance.md)
   : Performance Measures for Precision Matrix Estimation
+- [`plot(`*`<blkmat>`*`)`](https://shiying-xiao.com/grasps/reference/plot.blkmat.md)
+  : Plot Function for Block-Structured Precision Matrices (Visualize a
+  Matrix with Group Boundaries)
 - [`sparsify_block_banded()`](https://shiying-xiao.com/grasps/reference/sparsify_block_banded.md)
   : Groupwise Block-Banded Sparsifier
 

pkgdown.yml

@@ -3,7 +3,7 @@ pkgdown: 2.2.0
 pkgdown_sha: ~
 articles:
   pen_est: pen_est.html
-last_built: 2025-11-23T00:06Z
+last_built: 2025-11-23T02:51Z
 urls:
   reference: https://shiying-xiao.com/grasps/reference
   article: https://shiying-xiao.com/grasps/articles

reference/gen_prec_sbm-2.png

@@ -137,7 +137,7 @@ gen_prec_sbm(
 
 ## Value
 
-An object with S3 class "gen_prec_sbm" containing the following
+An object with S3 class `"gen_prec_sbm"` containing the following
 components:
 
 - Omega:
@@ -205,12 +205,27 @@ library(grasps)
 set.seed(1234)
 
 ## block-structured precision matrix based on SBM
-sim <- gen_prec_sbm(d = 100, K = 5,
-                    within.prob = 0.5, between.prob = 0.05,
-                    weight.dists = list("gamma", "unif"),
-                    weight.paras = list(c(shape = 20, scale = 5), c(min = 0, max = 1)),
-                    cond.target = 100)
-
-## visualization
-plot(sim)
+#### case 1: base R distribution
+sim1 <- gen_prec_sbm(d = 100, K = 5,
+                     within.prob = 0.5, between.prob = 0.05,
+                     weight.dists = list("gamma", "unif"),
+                     weight.paras = list(c(shape = 20, scale = 5),
+                                         c(min = 0, max = 1)),
+                     cond.target = 100)
+#### visualization
+plot(sim1)
+
+
+#### case 2: user-defined sampler
+my_gamma <- function(n) {
+  rgamma(n, shape = 10, scale = 5)
+}
+sim2 <- gen_prec_sbm(d = 100, K = 5,
+                     within.prob = 0.5, between.prob = 0.05,
+                     weight.dists = list(my_gamma, "unif"),
+                     weight.paras = list(NULL,
+                                         c(min = 0, max = 1)),
+                     cond.target = 100)
+#### visualization
+plot(sim2)
 ```

reference/gen_prec_sbm.html

@@ -227,7 +227,7 @@ grasps(
 
 ## Value
 
-An object with S3 class "grasps" containing the following components:
+An object with S3 class `"grasps"` containing the following components:
 
 - hatOmega:
 

reference/gen_prec_sbm.md

@@ -12,5 +12,8 @@
   Function Computation
 - [`performance()`](https://shiying-xiao.com/grasps/reference/performance.md)
   : Performance Measures for Precision Matrix Estimation
+- [`plot(`*`<blkmat>`*`)`](https://shiying-xiao.com/grasps/reference/plot.blkmat.md)
+  : Plot Function for Block-Structured Precision Matrices (Visualize a
+  Matrix with Group Boundaries)
 - [`sparsify_block_banded()`](https://shiying-xiao.com/grasps/reference/sparsify_block_banded.md)
   : Groupwise Block-Banded Sparsifier