# set some parameters
up <- 100 # upper bound
hat <- rep(150, 6) # estimates obtained from each model
sigmasq <- 10 # variance
Tstar <- matrix(rep(100,600),6,100) # T statistics estimated from bootstrap samples
weights <- rep(1/6, 6) # model weights
B <- 100 # number of bootstrapped samples
alpha <- 0.05 # confidence level
# calculate the upper limit of T statistics
res <- marp::upperT(up, hat, sigmasq, Tstar, weights, B, alpha)
# print result
cat("res = ", res, "\n")
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