Nothing
# context("Test samplers on likelihoods with infinities")
#
#
# bayesianSetup = createBayesianSetup(likelihood = testDensityInfinity, lower = c(0, 0), upper = c(5, 5))
#
# iter = 10000
# start = 500
# iterSMC = 400
#
# test_that("sampler work correct for likelihoods with infinities", {
#
# skip_on_cran()
#
# samp = getPossibleSamplerTypes()
#
# for(i in 1:length(samp$BTname)){
# # print(samp$BTname[i]) # Printing to console makes tests extremely slow
# if(samp$univariatePossible[i] == T){
# settings = list(iterations = iter, consoleUpdates = 1e+8)
# if(samp$BTname[i] == "SMC") settings = list(iterations = iterSMC, consoleUpdates = 1e+8)
# invisible(capture.output(suppressMessages(out <- runMCMC(bayesianSetup = setup, sampler = samp$BTname[i], settings = settings))))
#
# }
# # plot(out)
# # summary(out)
# # marginalPlot(out)
# # correlationPlot(out)
# # DIC(out)
# # marginalLikelihood(out)
#
# # x = getSample(out, numSamples = 10000)
# # y <- rnorm(10000) ## TODO change
# # for(z in 1:ncol(x)){
# #
# # # ks <- ks.test(x[,z], pnorm)$p.value
# #
# # ks <- ks.boot(x[,z], y)$ks.boot.pvalue
# #
# # # Test that distribution is not significally different from gaussian
# # expect_true(ks>0.05)
# #
# # }
#
# }
# }
# )
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