R Under development (unstable) (2025-02-03 r87683 ucrt) -- "Unsuffered Consequences"
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> library(Umpire)
> # set seed to ensure reproducibility
> suppressWarnings( RNGversion("3.5.3") )
> set.seed(823458)
> # create a noise model
> noise <- NoiseModel(0, 1, 0.1)
> # set parameters from independent normal hyperparameters
> nGenes <- 5
> for (i in 1:2) {
+   mu <- rnorm(nGenes, 6, 1)
+   sigma <- 1/rgamma(nGenes, shape=14, rate=5)
+   temp <- IndependentNormal(mu, sigma)
+   assign(paste('ind', i, sep=''), temp, 1)
+ }
> # create a correlation matrix
> a <- runif(1)
> b <- sqrt(1-a^2)
> X <- matrix(c(a, b, -b, a), 2, 2)
> Lambda2 <- diag(rev(sort(rexp(2))), 2)
> Y <- t(X) %*% Lambda2 %*% X
> # create  multivariate normal generator
> marvin <- MVN(c(0,0), Y)
> # create a  multi-component engine
> #old: engine <- Engine(ind1, ind2, marvin)
> engine <- Engine(list(ind1, ind2, marvin))
> # generate data from the engine
> x <- rand(engine, 10)
> # add noise
> y <- blur(noise, x)
> print(summary(as.vector(x-y)))
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-2.84937 -0.82289 -0.15102 -0.02792  0.56520  3.74870 
> # cleanup
> rm(ind1, ind2, mu, sigma, temp, i, nGenes,
+    a, b, Lambda2, X, Y, marvin, engine,
+    x, y, noise)
> 
>   
> 
> 
> proc.time()
   user  system elapsed 
   0.70    0.26    0.90