Nothing
# Make a random PMF.
set.seed(456)
val = seq(0, 15, len = 100)
pmf = data.frame(
val = val, P = dgamma(val, shape = 2, scale = 2) + runif(100) * 0.1)
pmf$P = pmf$P / sum(pmf$P)
completeRandomState = SimJoint::exportRandomState(456)
# `completeRandomState` comprises all the bits of a Mersenne Twister
# (C++11 std::mt19937_64) engine seeded by 456. It is similar to R's
# `.Random.seed`.
pmfSample1 = SimJoint::LHSpmf(pmf, 1000, completeRandomState)
pmfSample2 = SimJoint::LHSpmf(pmf, 1000, completeRandomState)
pmfSample3 = SimJoint::LHSpmf(pmf, 1000, completeRandomState)
# `completeRandomState` is changed in each run of `LHSpmf()`.
targetCor = rbind(
c(1, 0.3, 0.5),
c(0.3, 1, 0.3),
c(0.5, 0.3, 1))
result = SimJoint::SJpearson(
X = cbind(sort(pmfSample1), sort(pmfSample2), sort(pmfSample3)),
cor = targetCor, seed = completeRandomState, errorType = "maxRela")
cor(result$X)
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