## Not run:
# ## 1. Set parameters
# mciter <- 2 #500
# niter <- 10 #400000
# nodes <- 4
#
# ## 2. Setup parallel backend to use 4 processors
# library(foreach); library(doSNOW)
# cl <- makeCluster(4); registerDoSNOW(cl)
#
# ## 3. Define foreach loop function
# mce.add <- function(mciter, niter, N, n, m, type, method){
# h <- foreach(i=1:mciter) %dopar% {
# library(matchingMarkets)
# mce(seed=i,niter, N, n, m, type, method)
# }
# do.call(rbind, h)
# }
#
# ## 4. Run siumlations:
#
# ## 4-a. Benchmark study
# exp.5.5.ols <- mce.add(mciter=mciter, niter=niter, N=5, n=5, m=40,
# type="group.members", method="outcome")
# exp.5.5.ntu <- mce.add(mciter=mciter, niter=niter, N=5, n=5, m=40,
# type="group.members", method="NTU")
#
# ## 4-b. Experiment 1: randomly sampled group members
# exp.6.5.ols <- mce.add(mciter=mciter, niter=niter, N=6, n=5, m=40,
# type="group.members", method="outcome")
# exp.6.5.ntu <- mce.add(mciter=mciter, niter=niter, N=6, n=5, m=40,
# type="group.members", method="NTU")
#
# ## 4-c. Experiment 2: randomly sampled counterfactual groups
# exp.6.6.ols <- mce.add(mciter=mciter, niter=niter, N=6, n=6, m=40,
# type="counterfactual.groups", method="outcome")
# exp.6.6.ntu <- mce.add(mciter=mciter, niter=niter, N=6, n=6, m=40,
# type="counterfactual.groups", method="NTU")
#
# ## 5. Stop parallel backend
# stopCluster(cl)
# ## End(Not run)
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