utils::data("variogramModels")
v1 = setCgram(type=vg.Gau, sill=diag(2)+0.5, anisRanges = 2*diag(c(3,0.5)))
v2 = setCgram(type=vg.Exp, sill=0.3*diag(2), anisRanges = 0.5*diag(2))
vm = v1+v2
vgf = as.function(vm)
(h = rbind(c(0,1), c(0,0), c(1,1)))
vgf(h)
predict(vm, h)
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