R Under development (unstable) (2024-01-07 r85787 ucrt) -- "Unsuffered Consequences"
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> # test -- defineMUM()
> 
> library(spup)
> library(gstat)
> library(sp)
> 
> # test with data from gstat
> data(meuse)
> # make spatial
> coordinates(meuse) = ~x+y
> # make separate spatial grid data frames for mean and sd
> m1 <- meuse["zinc"]
> m1.sd <- m1
> m1.sd[[1]] <- m1.sd[[1]] * rnorm(n = 155, mean = 1, sd = 0.5)
> m2 <- meuse["lead"]
> m2.sd <- m2
> m2.sd[[1]] <- m2.sd[[1]] * rnorm(n = 155, mean = 1, sd = 0.5)
> # define srm and UM and sample
> crm1 <- makeCRM(acf0 = 0.6, range = 200, model = "Exp")
> crm2 <- makeCRM(acf0 = 0.7, range = 200, model = "Exp")
> UM1 <- defineUM(uncertain = TRUE, distribution = "norm",
+                 distr_param = c(m1, m1.sd), crm = crm1, id = "zinc")
> UM2 <- defineUM(uncertain = TRUE, distribution = "norm",
+                 distr_param = c(m2, m2.sd), crm = crm2, id = "lead")
> cormat <- matrix(c(1,0.7,0.7,1), nrow = 2, ncol = 2)
> JointUM <- defineMUM(list(UM1,UM2), cormatrix = cormat)	
> a <- genSample(UMobject = JointUM, n = 4, "ugs", asList = FALSE)							
Linear Model of Coregionalization found. Good.
[using unconditional Gaussian cosimulation]
> spplot(a)
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   8.64    0.45    9.07