Suppose I have a standardized data frame and I performed clustering using the R package diceR. The input format of the data frame and the clustering code are as follows. After clustering, I selected k = 9 as the result based on the silhouette coefficient. I have seen literature that suggests using the clusterboot function from the fpc package for resampling and calculating Jaccard similarity to evaluate the stability of the clustering. How can this method be implemented in R? Are there any better methods?

 structure(list(Age2 = c(1, 0, 1, 1, 1, 1, 1, 0, 1, 1), TMB2 = c(0, 
0, 0, 0, 0, 1, 0, 0, 0, 0), TP53 = c(1, 0, 0, 1, 1, 0, 1, 1, 
1, 1), PIK3CA = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 0), GATA3 = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0), MAP3K1 = c(0, 1, 0, 0, 0, 0, 0, 0, 
0, 0), NF1 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), RYR2 = c(0, 0, 
0, 0, 0, 0, 0, 0, 0, 0), PTEN = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 
0), ARID1A = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 0)), row.names = c(NA, 
10L), class = "data.frame")
set.seed(1234)
CC <-consensus_cluster(data, nk = 4:20, p.item = 0.8, reps = 10,
                       algorithms = c("hc","pam","km"),hc.method = "ward.D2")

Any help would be greatly appreciated.