# NOT RUN {
## 1. EXAMPLE
## Load the mnormt package to generate a multinormal dataset
## Dataset
Mean <- 0; nVar <- 2; ro <- 0
varcov <- matrix(c(rep(c(1, rep(ro, nVar)), nVar-1),1), nrow=nVar)
means <- rep(Mean, nVar)
X <- rmnorm(100,means,varcov)
data <- standardizeDataset(data.frame(X))
## Joint learnings
dim <- c(2,3)
param <- parametersJointMoTBF(X = data, dimensions = dim)
param$Parameters
length(param$Parameters)
param$Dimension
param$Range
P <- jointMoTBF(param)
P
attributes(P)
class(P)
# }
# NOT RUN {
###############################################################################
## MORE EXAMPLES ##############################################################
###############################################################################
# }
# NOT RUN {
## Load the mnormt package to generate a multinormal dataset
## Dataset
Mean <- 1; nVar <- 3; ro <- 0.5
varcov <- matrix(c(rep(c(1, rep(ro, nVar)), nVar-1),1), nrow=nVar)
means <- rep(Mean, nVar)
X <- rmnorm(200,means,varcov)
data <- standardizeDataset(data.frame(X))
## Joint learnings
dim <- c(3,2,4,2)
param <- parametersJointMoTBF(X = data, dimensions = dim)
param$Parameters
length(param$Parameters) ## prod(dim)
param$Dimension
param$Range
param$Time
P <- jointMoTBF(param)
P
attributes(P)
class(P)
# }
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