```
# Artificial data from an EEI model with k=2 components
library(mnormt)
p <- 2
k <- 2
eta <- c(8,8) # contamination parameters
set.seed(12345)
X1good <- rmnorm(n = 300, mean = rep(3,p), varcov = diag(c(5,0.5)))
X2good <- rmnorm(n = 300, mean = rep(-3,p), varcov = diag(c(5,0.5)))
X1bad <- rmnorm(n = 30, mean = rep(3,p), varcov = eta[1]*diag(c(5,0.5)))
X2bad <- rmnorm(n = 30, mean = rep(-3,p), varcov = eta[2]*diag(c(5,0.5)))
X <- rbind(X1good,X1bad,X2good,X2bad)
plot(X, pch = 16, cex = 0.8)
# model-based clustering with the whole family of 14
# parsimonious models and number of groups ranging from 1 to 3
overallfit <- MS(X, k = 1:2, model = c("EEI","VVV"), initialization = "mclust")
# to see the best BIC results
bestBIC <- overallfit$bestBIC
# plot of the best BIC model
plot(X, xlab = expression(X[1]), ylab = expression(X[2]), col = "white")
text(X, labels = bestBIC$detection$innergroup, col = bestBIC$group, cex = 0.7, asp = 1)
box(col = "black")
```

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