start.test <- function(name, wpp.year = NULL) cat('\n<=== Starting test of', name, if(!is.null(wpp.year)) paste0('(WPP ', wpp.year, ')') else '', '====\n')
test.ok <- function(name) cat('\n==== Test of', name, 'OK.===>\n')

test.load.UNtfr <- function(wpp.year=2008) {
	# read the UN TFR input file
	test.name <- 'loading UN TFR file'
	start.test(test.name, wpp.year)
	tfr <- bayesTFR:::read.UNtfr(wpp.year)
	stopifnot(length(dim(tfr$data.object$data))==2)
	stopifnot(dim(tfr$data.object$data)[1] > 150)
	stopifnot(is.element('last.observed', colnames(tfr$data.object$data)))
	stopifnot(length(tfr$data.object$replaced) == 0)
	stopifnot(length(tfr$data.object$added) == 0)
	test.ok(test.name)
}

test.load.UNtfr.and.my.tfr.file <- function() {
	# read the UN TFR input file for WPP 2019
	test.name <- 'loading UN TFR file and my_tfr_file'
	start.test(test.name)
	my.tfr.file <- file.path(find.package("bayesTFR"), 'extdata', 'my_tfr_template.txt')
	tfr <- bayesTFR:::read.UNtfr(2019, my.tfr.file=my.tfr.file)
	stopifnot(length(dim(tfr$data.object$data))==2)
	stopifnot(length(dim(tfr$suppl.data.object$data))==2)
	stopifnot(dim(tfr$data.object$data)[1] == 249)
	stopifnot(dim(tfr$suppl.data.object$data)[1] == 104)
	stopifnot(is.element('last.observed', colnames(tfr$data.object$data)))
	stopifnot(length(tfr$data.object$replaced) == 1)
	stopifnot(length(tfr$data.object$added) == 0)
	stopifnot(length(tfr$suppl.data.object$replaced) == 0)
	stopifnot(length(tfr$suppl.data.object$added) == 1)
	test.ok(test.name)
}

test.load.UNlocations <- function(wpp.year=2012) {
	test.name <- 'loading WPP location file'
	start.test(test.name, wpp.year)
	tfr <- suppressWarnings(bayesTFR:::read.UNtfr(wpp.year)) # if wpp.year=2008, there are warnings about non-existent tfr_supplemental dataset
	locs <- bayesTFR:::read.UNlocations(tfr$data.object$data, wpp.year) # this could give warnings if there are duplicates in UNlocations
	stopifnot(length(dim(locs$loc_data)) == 2)
	stopifnot(all(is.element(c('country_code', 'include_code'), colnames(locs$loc_data))))
	stopifnot(dim(locs$loc_data)[1] > 200)
	stopifnot(all(is.element(intersect(c(0,1,2), locs$loc_data[,'include_code']), c(0,1,2))))
	test.ok(test.name)
	
	test.name <- 'loading WPP location file with my.locations.file'
	start.test(test.name, wpp.year)
	new.locations <- data.frame(country_code=9999, name='my location', reg_code=9999, area_code=8888, reg_name='my region', area_name='my area', location_type=4)
	f <- tempfile()
	write.table(new.locations, file=f, sep='\t', row.names=FALSE)
	mylocs <- bayesTFR:::read.UNlocations(tfr$data.object$data, wpp.year, my.locations.file=f)
	# stopifnot(dim(mylocs$loc_data)[1] == dim(locs$loc_data)[1]+1) # has one country more
	# stopifnot(is.element(9999, mylocs$loc_data$country_code))
	e <- new.env()
	do.call("data", list("UNlocations", package=paste0("wpp", wpp.year), envir=e))
	# this for some reason causes a warning "‘match’ requires vector arguments"
	#data("UNlocations", package=paste0("wpp", wpp.year), envir=e) 
	my.locations <- e$UNlocations[1:100,]
	my.locations[which(my.locations$location_type == 4)[1],'area_code'] <- 123456
	write.table(my.locations, file=f, sep='\t', row.names=FALSE)
	mylocs <- bayesTFR:::read.UNlocations(tfr$data.object$data, wpp.year, my.locations.file=f)
	stopifnot(is.element(123456, mylocs$loc_data$area_code))
	test.ok(test.name)
	unlink(f)
}

test.create.tfr.matrix <- function(wpp.year=2008) {
	test.name <- 'creating TFR matrix'
	start.test(test.name, wpp.year)
	tfr <- bayesTFR:::read.UNtfr(wpp.year)
	locs <- bayesTFR:::read.UNlocations(tfr$data.object$data, wpp.year)
	tfr.and.regions <- bayesTFR:::get.TFRmatrix.and.regions(tfr$data.object$data, locs$loc_data, 
												present.year=2009)
	tfr.matrix <- tfr.and.regions$tfr_matrix
	stopifnot(dim(tfr.matrix)[1] == 12)
	stopifnot(rownames(tfr.matrix)[12] == '2008')
	test.ok(test.name)
}

test.run.mcmc.simulation <- function(compression='None', wpp.year = 2019) {
	sim.dir <- tempfile()

	# run MCMC
	test.name <- 'running Phase II MCMC'
	start.test(test.name, wpp.year)
	m <- run.tfr.mcmc(iter=5, nr.chains=1, output.dir=sim.dir, start.year=1950, 
	                  compression.type=compression, wpp.year = wpp.year)
	stopifnot(m$mcmc.list[[1]]$finished.iter == 5)
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 5)
	stopifnot(bayesTFR:::tfr.set.identical(m, get.tfr.mcmc(sim.dir), include.output.dir=FALSE))
	test.ok(test.name)

	# continue MCMC
	test.name <- 'continuing Phase II MCMC'
	start.test(test.name, wpp.year)
	m <- continue.tfr.mcmc(iter=5, output.dir=sim.dir)
	stopifnot(m$mcmc.list[[1]]$finished.iter == 10)
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 10)
	stopifnot(!is.element(900, m$meta$regions$country_code)) # 'World' should not be included
	test.ok(test.name)

	# run MCMC for an aggregation
	test.name <- 'running Phase II MCMC for extra areas'
	start.test(test.name, wpp.year)
	data.dir <- file.path(find.package("bayesTFR"), 'extdata')
	m <- run.tfr.mcmc.extra(sim.dir=sim.dir, 
			my.tfr.file=file.path(data.dir, 'my_tfr_template.txt'), burnin=0)
	stopifnot(is.element(900, m$meta$regions$country_code)) # 'World' should be included
	test.ok(test.name)

	test.name <- 'running Phase III MCMC'
	start.test(test.name, wpp.year)
	m3 <- run.tfr3.mcmc(sim.dir=sim.dir, iter=20, thin=1, nr.chains=2,  compression.type=compression)
	stopifnot(m3$mcmc.list[[1]]$finished.iter == 20)
	stopifnot(get.total.iterations(m3$mcmc.list, 0) == 40)
	stopifnot(bayesTFR:::tfr.set.identical(m3, get.tfr3.mcmc(sim.dir), include.output.dir=FALSE))
	test.ok(test.name)

	# continue MCMC
	test.name <- 'continuing Phase III MCMC'
	start.test(test.name, wpp.year)
	m3 <- continue.tfr3.mcmc(sim.dir=sim.dir, iter=5)
	stopifnot(m3$mcmc.list[[1]]$finished.iter == 25)
	stopifnot(get.total.iterations(m3$mcmc.list, 0) == 50)
	test.ok(test.name)

	# run prediction
	test.name <- 'running projections with classic AR(1)'
	start.test(test.name, wpp.year)
	pred <- tfr.predict(m, burnin=0, use.tfr3=FALSE, rho=NULL, sigmaAR1=NULL, verbose=FALSE)
	spred <- summary(pred)
	stopifnot(spred$nr.traj == 10)
	stopifnot(is.na(pred$thin3))
	stopifnot(!is.element(903, pred$mcmc.set$regions$country_code))
	test.ok(test.name)
	
	rho <- pred$rho
	sigma <- pred$sigmaAR1
	mu <- pred$mu
	
	test.name <- 'running projections with BHM for phase III'
	start.test(test.name, wpp.year)
	pred <- tfr.predict(m, burnin=0, use.tfr3=TRUE, burnin3=5, verbose=FALSE, replace.output=TRUE)
	spred <- summary(pred)
	stopifnot(spred$nr.traj == 10)
	stopifnot(pred$thin3 == 4) 
	stopifnot(!is.element(903, pred$mcmc.set$regions$country_code))
	test.ok(test.name)
	npred <- dim(pred$tfr_matrix_reconstructed)[2]
	
	# run MCMC for another aggregation
	test.name <- 'running projections on extra areas'
	start.test(test.name, wpp.year)
	m <- run.tfr.mcmc.extra(sim.dir=sim.dir, countries=903, burnin=0)
	# run prediction only for the area 903
	pred <- tfr.predict.extra(sim.dir=sim.dir, verbose=FALSE)
	stopifnot(is.element(903, pred$mcmc.set$meta$regions$country_code))
	stopifnot(dim(pred$tfr_matrix_reconstructed)[2] == npred+1)
	stopifnot(!is.null(bayesTFR:::get.trajectories(pred, 903)$trajectories))
	stopifnot(pred$use.tfr3) 
	test.ok(test.name)

	test.name <- 'estimating AR(1) parameters'
	start.test(test.name, wpp.year)
	ar1pars <- get.ar1.parameters(mu, m$meta)
	stopifnot(rho==ar1pars$rho)
	stopifnot(sigma==ar1pars$sigmaAR1)
	test.ok(test.name)

	test.name <- 'shifting the median'
	start.test(test.name, wpp.year)
	projs <- summary(pred, country='Uganda')$projections
	tfr.median.shift(sim.dir, country='Uganda', shift=1.5, from=2051, to=2080)
	shifted.pred <- get.tfr.prediction(sim.dir)
	shifted.projs <- summary(shifted.pred, country='Uganda')$projections
	stopifnot(all(projs[8:13,c(1,3:dim(projs)[2])]+1.5 == shifted.projs[8:13,c(1,3:dim(projs)[2])]))
	stopifnot(all(projs[c(1:7, 14:17),c(1,3:dim(projs)[2])] == shifted.projs[c(1:7, 14:17),c(1,3:dim(projs)[2])]))
	test.ok(test.name)

	test.name <- 'resetting the median'
	start.test(test.name, wpp.year)
	shifted.pred <- tfr.median.shift(sim.dir, country='Uganda', reset = TRUE)
	shifted.projs <- summary(shifted.pred, country='Uganda')$projections
	stopifnot(all(projs[,c(1,3:dim(projs)[2])] == shifted.projs[,c(1,3:dim(projs)[2])]))
	test.ok(test.name)

	test.name <- 'setting the median'
	start.test(test.name, wpp.year)
	expert.values <- c(2.3, 2.4, 2.4)
	cobj <- get.country.object('Uganda', m$meta)
	shift <- expert.values - pred$quantiles[cobj$index, '0.5',2:4]
	mod.pred <- tfr.median.set(sim.dir, country='Uganda', values=expert.values, years=2024)
	mod.projs <- summary(mod.pred, country='Uganda')$projections
	stopifnot(all(mod.projs[2:4, c(1,3:dim(projs)[2])]==projs[2:4, c(1,3:dim(projs)[2])]+shift))
	stopifnot(all(mod.projs[c(1,5:17), c(1,3:dim(projs)[2])]==projs[c(1,5:17), c(1,3:dim(projs)[2])]))
	test.ok(test.name)
	
	test.name <- 'shifting medians to WPP'
	tfr.shift.prediction.to.wpp(sim.dir)
	shifted.pred <- get.tfr.prediction(sim.dir)
	shifted.projs <- summary(shifted.pred, country='Niger')$projections
	shifted.projs <- data.table::data.table(shifted.projs)[, year := as.integer(rownames(shifted.projs))]
	e <- new.env()
	data("tfrprojMed", package = paste0("wpp", wpp.year), envir = e)
	wpptfr <- data.table::data.table(e$tfrprojMed)
	wpptfrl <- data.table::melt(wpptfr, id.vars = c("country_code", "name"), variable.name = "period")
	wpptfrl <- wpptfrl[, year := as.integer(substr(period, 1,4)) + 3][name == "Niger"]
	dat <- merge(wpptfrl, shifted.projs[, c("year", "50%"), with = FALSE], by = "year")
	stopifnot(all.equal(dat$value, dat[, `50%`]))
	stopifnot(!is.null(shifted.pred$median.shift))
	stopifnot(length(shifted.pred$median.shift) == nrow(get.countries.table(shifted.pred)))
	test.ok(test.name)
	
	test.name <- 'resetting all countries'
	tfr.median.reset(sim.dir)
	new.pred <- get.tfr.prediction(sim.dir)
	stopifnot(is.null(new.pred$median.shift))
	test.ok(test.name)
	
	unlink(sim.dir, recursive=TRUE)
}

test.thinned.simulation <- function(compression='None', wpp.year = 2019) {
	sim.dir <- tempfile()
	# run MCMC
	test.name <- 'running thinned Phase II MCMC'
	start.test(test.name, wpp.year)
	m <- run.tfr.mcmc(iter=10, nr.chains=2, output.dir=sim.dir, thin=2, compression.type=compression,
	                  wpp.year = wpp.year)
	stopifnot(m$mcmc.list[[1]]$finished.iter == 10)
	stopifnot(m$mcmc.list[[1]]$length == 6)
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 20)
	stopifnot(get.stored.mcmc.length(m$mcmc.list, burnin=4) == 6)
	test.ok(test.name)

	# continue MCMC
	test.name <- 'continuing thinned Phase II MCMC'
	start.test(test.name, wpp.year)
	m <- continue.tfr.mcmc(iter=10, output.dir=sim.dir)
	stopifnot(m$mcmc.list[[1]]$finished.iter == 20)
	stopifnot(m$mcmc.list[[1]]$length == 11)
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
	stopifnot(get.stored.mcmc.length(m$mcmc.list, burnin=4) == 16)
	test.ok(test.name)

	# run MCMC for an aggregation
	test.name <- 'running thinned MCMC for extra areas'
	start.test(test.name, wpp.year)
	data.dir <- file.path(find.package("bayesTFR"), 'extdata')
	m <- run.tfr.mcmc.extra(sim.dir=sim.dir, my.tfr.file=file.path(data.dir, 'my_tfr_template.txt'), burnin=0)
	stopifnot(is.element(900, m$meta$regions$country_code)) # 'World' should be included
	test.ok(test.name)

	test.name <- 'running thinned Phase III MCMC'
	start.test(test.name, wpp.year)
	m3 <- run.tfr3.mcmc(sim.dir=sim.dir, iter=20, nr.chains=3, thin=4, compression.type=compression)
	stopifnot(m3$mcmc.list[[1]]$finished.iter == 20)
	stopifnot(m3$mcmc.list[[1]]$length == 6)
	stopifnot(get.total.iterations(m3$mcmc.list, 0) == 60)
	stopifnot(get.stored.mcmc.length(m3$mcmc.list, burnin=4) == 12)
	test.ok(test.name)

	# continue MCMC
	test.name <- 'continuing thinned Phase III MCMC'
	start.test(test.name, wpp.year)
	m3 <- continue.tfr3.mcmc(sim.dir=sim.dir, iter=5)
	stopifnot(m3$mcmc.list[[1]]$finished.iter == 25)
	stopifnot(m3$mcmc.list[[1]]$length == 7)
	stopifnot(get.total.iterations(m3$mcmc.list, 0) == 75)
	stopifnot(get.stored.mcmc.length(m3$mcmc.list, burnin=4) == 15)
	test.ok(test.name)

	# run prediction
	test.name <- 'running thinned projections'
	start.test(test.name, wpp.year)
	pred <- tfr.predict(m, burnin=5, burnin3=3, verbose=FALSE)
	spred <- summary(pred)
	stopifnot(spred$nr.traj == 16) # 2x8
	stopifnot(pred$thin3 == 4)
	stopifnot(pred$mcmc.set$mcmc.list[[1]]$finished.iter == 16)
	stopifnot(length(pred$mcmc.set$mcmc.list) == 1)
	npred <- dim(pred$tfr_matrix_reconstructed)[2]
	test.ok(test.name)

	test.name <- 'running thinned projections on extra areas'
	start.test(test.name, wpp.year)
	m <- run.tfr.mcmc.extra(sim.dir=sim.dir, countries=903, burnin=5)
	# run prediction only for the area 903
	pred <- tfr.predict.extra(sim.dir=sim.dir, verbose=FALSE)
	stopifnot(dim(pred$tfr_matrix_reconstructed)[2] == npred+1)
	test.ok(test.name)

	test.name <- 'plotting DL curves with thinned MCMC'
	start.test(test.name, wpp.year)
	filename <- tempfile()
	png(filename=filename)
	DLcurve.plot(m, 903, burnin=5, nr.curves=5)
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)

	test.name <- 'plotting TFR trajectories with thinned MCMC'
	start.test(test.name, wpp.year)
	filename <- tempfile()
	png(filename=filename)
	tfr.trajectories.plot(pred, 900, nr.traj=5, pi=c(70,65))
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)

	test.name <- 'getting Phase II parameter traces with thinned MCMC'
	start.test(test.name, wpp.year)
	traces <- get.tfr.parameter.traces(m$mcmc.list, burnin=5, 
					thinning.index=c(1, 3, 10, 15))
	stopifnot(nrow(traces)==4)
	test.ok(test.name)
	
	test.name <- 'getting Phase III parameter traces with thinned MCMC'
	start.test(test.name, wpp.year)
	m3 <- get.tfr3.mcmc(sim.dir)
	traces <- get.tfr3.parameter.traces(m3$mcmc.list, burnin=10, 
					thinning.index=c(1, 3, 5, 10))
	stopifnot(nrow(traces)==4)
	test.ok(test.name)

	test.name <- 'plotting Phase II parameter density'
	start.test(test.name, wpp.year)
	filename <- tempfile()
	png(filename=filename)
	tfr.pardensity.plot(m, burnin=10)
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
	
	test.name <- 'plotting Phase III parameter density'
	start.test(test.name, wpp.year)
	filename <- tempfile()
	png(filename=filename)
	tfr3.pardensity.plot(m3, burnin=10)
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)

	unlink(sim.dir, recursive=TRUE)
}

test.run.mcmc.simulation.auto <- function() {
	sim.dir <- tempfile()
	# run MCMC
	test.name <- 'running auto Phase II MCMC'
	start.test(test.name)
	m <- run.tfr.mcmc(iter='auto', output.dir=sim.dir,
					auto.conf=list(iter=10, iter.incr=5, max.loops=3, nr.chains=2, thin=1, burnin=5))
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
	test.ok(test.name)

	test.name <- 'continuing auto Phase II MCMC'
	start.test(test.name)
	m <- continue.tfr.mcmc(iter='auto', output.dir=sim.dir, auto.conf=list(max.loops=2))
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 60)
	test.ok(test.name)
	
	test.name <- 'running auto Phase III MCMC'
	start.test(test.name)
	m3 <- run.tfr3.mcmc(sim.dir=sim.dir, iter='auto', thin=1,
					auto.conf=list(iter=10, iter.incr=5, max.loops=3, nr.chains=2, thin=1, burnin=5))
	stopifnot(get.total.iterations(m3$mcmc.list, 0) == 40)
	test.ok(test.name)

	test.name <- 'continuing auto Phase III MCMC'
	start.test(test.name)
	m3 <- continue.tfr3.mcmc(sim.dir=sim.dir, iter='auto', auto.conf=list(max.loops=2))
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 60)
	test.ok(test.name)

	unlink(sim.dir, recursive=TRUE)
}

test.run.mcmc.simulation.auto.parallel <- function() {
	sim.dir <- tempfile()
	# run MCMC
	test.name <- 'running auto Phase II MCMC in parallel'
	start.test(test.name)
	m <- run.tfr.mcmc(iter='auto', output.dir=sim.dir, parallel=TRUE, cltype='SOCK',
					auto.conf=list(iter=10, iter.incr=5, max.loops=3, nr.chains=2, thin=1, burnin=5))
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
	test.ok(test.name)

	test.name <- 'continuing auto Phase II MCMC in parallel'
	start.test(test.name)
	m <- continue.tfr.mcmc(iter='auto', output.dir=sim.dir, auto.conf=list(max.loops=2), parallel=TRUE, cltype='SOCK')
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 60)
	test.ok(test.name)
	
	test.name <- 'running auto Phase III MCMC in parallel'
	start.test(test.name)
	m3 <- run.tfr3.mcmc(sim.dir=sim.dir, iter='auto', thin=1, parallel=TRUE, cltype='SOCK',
					auto.conf=list(iter=10, iter.incr=5, max.loops=3, nr.chains=2, thin=1, burnin=5))
	stopifnot(get.total.iterations(m3$mcmc.list, 0) == 40)
	test.ok(test.name)

	test.name <- 'continuing auto Phase III MCMC in parallel'
	start.test(test.name)
	m3 <- continue.tfr3.mcmc(sim.dir=sim.dir, iter='auto', auto.conf=list(max.loops=2), parallel=TRUE, cltype='SOCK' )
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 60)
	test.ok(test.name)

	test.name <- 'running auto Phase II MCMC in parallel with ClusterOptions'
	start.test(test.name)
	#snow::setDefaultClusterOptions(type='SOCK')
	m <- run.tfr.mcmc(iter='auto', output.dir=sim.dir, parallel=TRUE, replace.output=TRUE,
					auto.conf=list(iter=10, iter.incr=5, max.loops=3, nr.chains=2, thin=1, burnin=5))
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
	test.ok(test.name)

	unlink(sim.dir, recursive=TRUE)
}

test.imputation <- function() {
	sim.dir <- tempfile()

	# run MCMC
	test.name <- 'running MCMC with missing values'
	start.test(test.name)
	my.tfr.file <- file.path(find.package('bayesTFR'), 'extdata', 'UN2010_last_obs.txt')
	m <- run.tfr.mcmc(iter=5, nr.chains=1, output.dir=sim.dir, my.tfr.file=my.tfr.file, wpp.year=2010, present.year=2010)
	stopifnot(m$mcmc.list[[1]]$finished.iter == 5)
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 5)
	stopifnot(bayesTFR:::tfr.set.identical(m, get.tfr.mcmc(sim.dir), include.output.dir=FALSE))
	# some countries are not DL because of the missing data
	# This was the case in UN2008 but not in UN2010
	# stopifnot(length(get.countries.index(m$meta)) != get.nr.countries(m$meta))
	
	m3 <- run.tfr3.mcmc(sim.dir=sim.dir, iter=10, nr.chains=1, thin=1, my.tfr.file=my.tfr.file)
	stopifnot(m3$mcmc.list[[1]]$finished.iter == 10)
	stopifnot(get.total.iterations(m3$mcmc.list, 0) == 10)
	stopifnot(bayesTFR:::tfr.set.identical(m3, get.tfr3.mcmc(sim.dir), include.output.dir=FALSE))
	test.ok(test.name)
	
	test.name <- 'running projections with imputation'
	start.test(test.name)
	pred <- tfr.predict(m, burnin=0, burnin3=3, verbose=FALSE)
	spred <- summary(pred)
	stopifnot(spred$nr.traj == 5)
	test.ok(test.name)
	
	test.name <- 'plotting imputed TFR trajectories'
	start.test(test.name)
	filename <- tempfile()
	png(filename=filename)
	tfr.trajectories.plot(pred, 'Eritrea', pi=c(90, 54))
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)

	test.name <- 'creating parameter maps with imputation'
	filename <- tempfile()
	tfr.map(pred, device='png', par.name='Triangle_c4', device.args=list(filename=filename))
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
	
	unlink(sim.dir, recursive=TRUE)
}

test.existing.simulation <- function() {
	test.name <- 'retrieving Phase II MCMC results'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	m <- get.tfr.mcmc(sim.dir, low.memory=FALSE, burnin=25, chain.ids=1)
	stopifnot(length(m$mcmc.list)==1)
	stopifnot(dim(m$mcmc.list[[1]]$traces)[1]==35)
	test.ok(test.name)
}

test.DLcurve <- function() {
	test.name <- 'plotting DL curves'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	m <- get.tfr.mcmc(sim.dir)
	filename <- tempfile()
	png(filename=filename)
	DLcurve.plot(m, 'Burkina Faso')
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
	test.name <- 'obtaining DL curves and DL sigma'
	start.test(test.name)
	tfr <- seq(1, 8, length=100)
	dl <- tfr.country.dlcurves(tfr, m, "Nigeria", burnin=10)
	stopifnot(all(dim(dl)==c(50,100)))
	dls <- tfr.country.dlcurves(tfr, m, "Nigeria", burnin=0, return.sigma=TRUE)
	stopifnot(all(dim(dls$sigma)==c(60,100)))
	# world distribution
	dlw <- tfr.world.dlcurves(tfr, m, countryUc="Nigeria")
	stopifnot(all(dim(dlw)==c(60,100)))
	# median of the world DL in the TFR range of 3-4 is smaller than the country-specific median in that range
	# visual check:
	# DLcurve.plot(m, 'Nigeria')
	# lines(tfr, apply(dlw, 2, median))
	stopifnot(all(apply(dlw[, tfr > 3 & tfr < 4], 2, median) < apply(dls$dl[, tfr > 3 & tfr < 4], 2, median)))
	test.ok(test.name)
}

test.TFRtrajectories <- function() {
	test.name <- 'plotting TFR trajectories'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	pred <- get.tfr.prediction(sim.dir=sim.dir)
	filename <- tempfile()
	png(filename=filename)
	tfr.trajectories.plot(pred, 'Australia')
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)

	test.name <- 'tabulating TFR trajectories'
	start.test(test.name)
	t <- tfr.trajectories.table(pred, 'Australia', pi=c(90, 80, 70))
	stopifnot(all(dim(t) == c(30, 9)))
	test.ok(test.name)
}

test.plot.all <- function() {
	test.name <- 'plotting TFR trajectories and DL curves for all countries'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	pred <- get.tfr.prediction(sim.dir=sim.dir)
	mc <- get.tfr.mcmc(sim.dir)
	dir <- tempfile()
	tfr.trajectories.plot.all(pred, output.dir=dir, main='XXX trajs')
	trajf <- length(list.files(dir, pattern='png$', full.names=FALSE))
	DLcurve.plot.all(mc, output.dir=dir, main='DL XXX', output.type='jpeg')
	dlf <- length(list.files(dir, pattern='jpeg$', full.names=FALSE))
	unlink(dir, recursive=TRUE)
	stopifnot(trajf == get.nr.countries(mc$meta))
	stopifnot(dlf == length(mc$meta$id_DL))
	test.ok(test.name)
}


test.plot.density <- function() {
	test.name <- 'plotting parameter density'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	pred <- get.tfr.prediction(sim.dir=sim.dir)
	filename <- tempfile()
	png(filename=filename)
	tfr.pardensity.cs.plot('Ireland', pred)
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
}

test.plot.map <- function() {
	test.name <- 'creating TFR maps via rworldmap'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	pred <- get.tfr.prediction(sim.dir=sim.dir)
	filename <- tempfile()
	tfr.map(pred, year=2043, device='png', device.args=list(filename=filename))
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
	
	test.name <- 'creating parameter maps'
	filename <- tempfile()
	tfr.map(pred, year=1985, device='png', par.name='gamma_2', device.args=list(filename=filename))
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
	
	test.name <- 'creating lambda maps'
	filename <- tempfile()
	tfr.map(pred, device='png', par.name='lambda', quantile=0.7, 
			device.args=list(filename=filename), numCats=20, catMethod="pretty")
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
	
	test.name <- 'creating TFR maps using ggplot2'
	filename <- paste0(tempfile(), ".png")
	tfr.ggmap(pred, file.name = filename)
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	tfr.ggmap(pred, same.scale = TRUE, file.name = filename)
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	tfr.ggmap(pred, same.scale = TRUE, year = 2100, file.name = filename)
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)
}
	
test.get.parameter.traces <- function() {
	test.name <- 'getting parameter traces'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	m <- get.tfr.mcmc(sim.dir, low.memory=TRUE)
	traces <- get.tfr.parameter.traces(m$mcmc.list, burnin=15, 
					thinning.index=c(4, 25, 29))
	stopifnot(nrow(traces)==3)
	m.check <- get.tfr.mcmc(sim.dir, low.memory=FALSE, burnin=15)
	stopifnot(traces[1,'chi']==m.check$mcmc.list[[1]]$traces[4,'chi'])
	#stopifnot(all(traces[c(2,3),'chi']==m.check$mcmc.list[[2]]$traces[c(10,14),'chi'])) # in case of two chains
	stopifnot(all(traces[c(2,3),'chi']==m.check$mcmc.list[[1]]$traces[c(25,29),'chi']))
	
	m.check <- get.tfr.mcmc(sim.dir, low.memory=FALSE, burnin=30)
	traces <- get.tfr.parameter.traces(m$mcmc.list, burnin=30, thin=8)
	stopifnot(nrow(traces)==4)
	stopifnot(traces[2,'psi']==m.check$mcmc.list[[1]]$traces[9,'psi'])
	#stopifnot(traces[4,'psi']==m.check$mcmc.list[[2]]$traces[10,'psi'])
	stopifnot(traces[4,'psi']==m.check$mcmc.list[[1]]$traces[25,'psi'])
	test.ok(test.name)
}

test.median.adjust <- function() {
	test.name <- 'adjusting median'
	start.test(test.name)
	sim.dir <- file.path(find.package("bayesTFR"), "ex-data", 'bayesTFR.output')
	m <- get.tfr.mcmc(sim.dir)
	pred.dir <- tempfile()
	pred <- tfr.predict(m, burnin=0, output.dir=pred.dir, nr.traj=10, save.as.ascii=0, verbose=FALSE, use.tfr3=FALSE)
	country.obj1 <- get.country.object('Kenya', m$meta)
	country.obj2 <- get.country.object('Mongolia', m$meta)
	new.pred <- tfr.median.adjust(pred.dir, countries=c('Kenya', 'Mongolia'))
	orig.median1 <- get.median.from.prediction(new.pred, country.obj1$index, country.obj1$code, adjusted=FALSE)
	new.median1 <- get.median.from.prediction(new.pred, country.obj1$index, country.obj1$code, adjusted=TRUE)
	orig.median2 <- get.median.from.prediction(new.pred, country.obj2$index, country.obj2$code, adjusted=FALSE)
	new.median2 <- get.median.from.prediction(new.pred, country.obj2$index, country.obj2$code, adjusted=TRUE)
	stopifnot(sum(orig.median1==new.median1)==1) # only the present year should be equal
	stopifnot(sum(orig.median2==new.median2)==1)
	test.ok(test.name)

	test.name <- 'plotting adjusted TFR trajectories'
	start.test(test.name)
	filename <- tempfile()
	png(filename=filename)
	tfr.trajectories.plot(new.pred, 'Kenya', adjusted.only=FALSE)
	dev.off()
	size <- file.info(filename)['size']
	unlink(filename)
	stopifnot(size > 0)
	test.ok(test.name)

	test.name <- 'writing summary files'
	start.test(test.name)
	write.projection.summary(sim.dir, adjusted=FALSE, output.dir=pred.dir)
	write.projection.summary(sim.dir, adjusted=TRUE, output.dir=pred.dir)
	test.ok(test.name)
	unlink(pred.dir)
}

test.estimate.mcmc.with.suppl.data <- function() {
	sim.dir <- tempfile()
   	# run MCMC
    test.name <- 'estimating MCMC using supplemental data'
    start.test(test.name)
    m <- run.tfr.mcmc(nr.chains=1, iter=30, thin=1, output.dir=sim.dir, start.year=1750, seed=1)
    stopifnot(length(m$meta$suppl.data$regions$country_code) == 103)
	stopifnot(all(dim(m$meta$suppl.data$tfr_matrix) == c(40, 103)))
    test.ok(test.name)
        
	# continue MCMC
	test.name <- 'continuing MCMC with supplemental data'
	start.test(test.name)
	m <- continue.tfr.mcmc(iter=10, output.dir=sim.dir)
	stopifnot(m$mcmc.list[[1]]$finished.iter == 40)
	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
	stopifnot(!is.element(900, m$meta$regions$country_code)) # 'World' should not be included
	test.ok(test.name)
        
	# run MCMC for an aggregation
	test.name <- 'estimating MCMC for extra areas with supplemental data'
	start.test(test.name)
	data.dir <- file.path(find.package("bayesTFR"), 'extdata')
	m <- run.tfr.mcmc.extra(sim.dir=sim.dir, 
                            my.tfr.file=file.path(data.dir, 'my_tfr_template.txt'), burnin=0, verbose=TRUE)
	stopifnot(is.element(900, m$meta$regions$country_code)) # 'World' should be included
	stopifnot(is.element(900, m$meta$suppl.data$regions$country_code))
	test.ok(test.name)
        
	# run prediction
	test.name <- 'running projections for simulation with supplemental data'
	start.test(test.name)
	pred <- tfr.predict(m, burnin=10, verbose=FALSE, save.as.ascii=0, rho=NULL, sigmaAR1=NULL, use.tfr3=FALSE)
	spred <- summary(pred)
	stopifnot(spred$nr.traj == 30)
	stopifnot(!is.element(903, pred$mcmc.set$regions$country_code))
	npred <- dim(pred$tfr_matrix_reconstructed)[2]
	test.ok(test.name)
	unlink(sim.dir, recursive=TRUE)
}

test.subnational.predictions <- function() {
  sim.dir <- tempfile()
  test.name <- 'predicting subnational TFR'
  start.test(test.name)
  
  my.subtfr.file <- file.path(find.package("bayesTFR"), 'extdata', 'subnational_tfr_template.txt')
  nat.dir <- file.path(find.package("bayesTFR"), "ex-data", "bayesTFR.output")
  
  # Subnational projections for Australia and Canada; start 1 time period before national projections
  preds <- tfr.predict.subnat(c(36, 124), my.tfr.file=my.subtfr.file,
                              sim.dir=nat.dir, output.dir=sim.dir, start.year=2011)
  stopifnot(all(names(preds) %in% c("36", "124")))
  stopifnot(nrow(get.countries.table(preds[["36"]]))==8)
  filename <- tempfile()
  pdf(file=filename)
  tfr.trajectories.plot(preds[["36"]], "Queensland")
  tfr.trajectories.plot(preds[["124"]], "Quebec")
  dev.off()
  size <- file.info(filename)['size']
  unlink(filename)
  stopifnot(size > 0)
  unlink(sim.dir, recursive=TRUE)
  
  # Subnational projections for Canada; start year after the start of national projections
  sim.dir <- tempfile()
  preds <- tfr.predict.subnat("Canada", my.tfr.file=my.subtfr.file,
                              sim.dir=nat.dir, output.dir=sim.dir, start.year=2018)
  stopifnot(all(names(preds) %in% c("124")))
  spred <- summary(preds[["124"]], "Quebec")
  stopifnot(min(spred$projection.years) == 2018)
  unlink(sim.dir, recursive=TRUE)
  
  # Subnational projections for Canada with one region (Alberta) having imputed values
  sim.dir <- tempfile()
  mytfr <- read.delim(my.subtfr.file, check.names = FALSE)
  mytfr[mytfr$country_code == 124 & mytfr$reg_code == 658, "last.observed"] <- 1999
  my.subtfr.file.mis <- tempfile()
  write.table(mytfr, file = my.subtfr.file.mis, row.names = FALSE, quote = FALSE, sep = "\t")
  preds <- tfr.predict.subnat("Canada", my.tfr.file=my.subtfr.file.mis,
                              sim.dir=nat.dir, output.dir=sim.dir, start.year=2013)
  obs <- preds[["124"]]$mcmc.set$meta$tfr_matrix_observed[,"658"]
  recon <- preds[["124"]]$tfr_matrix_reconstructed[,"658"]
  stopifnot(all(is.na(obs[c("2003", "2008")]))) # observed is NA
  stopifnot(all(!is.na(obs["1998"]))) # last observed non-NA 
  stopifnot(all(!is.na(recon[c("2003", "2008")]))) # missing values were reconstructed
  unlink(sim.dir, recursive=TRUE)
  unlink(my.subtfr.file.mis)
  
  # Subnational projections for all countries in the file
  sim.dir <- tempfile()
  preds <- tfr.predict.subnat(c(36, 218, 124), my.tfr.file=my.subtfr.file, sim.dir=nat.dir, 
                              output.dir=sim.dir, end.year=2050)
  stopifnot(length(preds) == 3)
  
  # Retrieve results for all countries
  preds <- get.regtfr.prediction(sim.dir)
  stopifnot(length(preds) == 3)
  t <- tfr.trajectories.table(preds[["218"]], "Bolivar")
  stopifnot(all(dim(t) == c(17, 7)))
  
  # Retrieve results for one country
  pred <- get.regtfr.prediction(sim.dir, 218)
  spred <- summary(pred, "Loja")
  stopifnot(max(spred$projection.years) == 2048)
  
  # Retrieve trajectories
  trajs <- get.tfr.trajectories(preds[["124"]], "Alberta")
  stopifnot(all(dim(trajs) == c(7, 30)))
  
  unlink(sim.dir, recursive=TRUE)
  
  # Annual subnational projections
  my.subtfr.file.annual <- tempfile()
  dat <- data.table::fread(my.subtfr.file) # load the data and save only the last time period, pretending this is a single year data point
  dat <- dat[, .(country_code, country, reg_code, name, `2008` = `2005-2010`)]
  data.table::fwrite(dat, file = my.subtfr.file.annual, sep = "\t") # save it
  
  sim.dir <- tempfile()
  preds <- tfr.predict.subnat(c(36, 218, 124), my.tfr.file=my.subtfr.file.annual, sim.dir=nat.dir, 
                              output.dir=sim.dir, start.year = 2009, end.year=2030, annual = TRUE)

  # Retrieve trajectories
  trajs <- get.tfr.trajectories(preds[["124"]], "Alberta")
  stopifnot(all(dim(trajs) == c(23, 30))) # from 2008 to 2030
  stopifnot(all(c(2021, 2029) %in% as.integer(rownames(trajs))))
  
  pred <- get.regtfr.prediction(sim.dir, 218)
  spred <- summary(pred, "Manabi")
  stopifnot(max(spred$projection.years) == 2030)
  
  t <- tfr.trajectories.table(preds[["124"]], "Ontario")
  stopifnot(max(as.integer(rownames(t))) == 2030)
  stopifnot(all(c(2009, 2014, 2029) %in% as.integer(rownames(t))))
  
  unlink(sim.dir, recursive=TRUE)
  unlink(my.subtfr.file.annual)
  
  test.ok(test.name)
  
}

test.reproduce.simulation <- function() {
    sim.dir <- tempfile()
    test.name <- 'reproducing simulation'
    start.test(test.name)
    seed <- 1234
    m1 <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, seed = seed)
    res1 <- summary(m1)$phase2$results$statistics
    m2 <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, seed = seed, replace.output = TRUE)
    res2 <- summary(m2)$phase2$results$statistics
    stopifnot(!is.null(res1) && all(res1 == res2))
    
    m3 <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, replace.output = TRUE) # no seed
    res3 <- summary(m3)$phase2$results$statistics
    stopifnot(!all(res3 == res2))
    
    # parallel
    m1p <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, seed = seed, replace.output = TRUE, parallel = TRUE, ft_verbose = TRUE)
    res1p <- summary(m1p)$phase2$results$statistics
    m2p <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, seed = seed, replace.output = TRUE, parallel = TRUE, ft_verbose = TRUE)
    res2p <- summary(m2p)$phase2$results$statistics
    stopifnot(!is.null(res1p) && all(res1p == res2p))
    
    m3p <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, replace.output = TRUE, parallel = TRUE, ft_verbose = TRUE) # no seed
    res3p <- summary(m3p)$phase2$results$statistics
    stopifnot(!all(res3p == res2p))
    
    # with uncertainty
    mu1 <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, seed = seed, uncertainty = TRUE, replace.output = TRUE)
    resu1 <- summary(mu1)$phase2$results$statistics
    mu2 <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, seed = seed, uncertainty = TRUE, replace.output = TRUE)
    resu2 <- summary(mu2)$phase2$results$statistics
    mu3 <- run.tfr.mcmc(iter=5, nr.chains=2, output.dir=sim.dir, start.year=1950, uncertainty = TRUE, replace.output = TRUE)
    stopifnot(!is.null(resu1) && all(resu1 == resu2)) # includes both phase 2 and 3 parameters 
    stopifnot(!all(resu1 == summary(mu3)$phase2$results$statistics))
    
    test.ok(test.name)
    unlink(sim.dir, recursive=TRUE)
}

test.run.mcmc.simulation.with.uncertainty <- function(wpp.year = 2019) {
    sim.dir <- tempfile()

    # run MCMC
    test.name <- 'running MCMC with uncertainty'
    start.test(test.name, wpp.year)
    m <- run.tfr.mcmc(iter=5, nr.chains=1, output.dir=sim.dir, uncertainty = TRUE, wpp.year = wpp.year)
    stopifnot(m$mcmc.list[[1]]$finished.iter == 5)
    stopifnot(get.total.iterations(m$mcmc.list, 0) == 5)
    stopifnot(bayesTFR:::tfr.set.identical(m, get.tfr.mcmc(sim.dir), include.output.dir=FALSE))
    
    # continue MCMC
    test.name <- 'continuing MCMC with uncertainty'
    start.test(test.name, wpp.year)
    m <- continue.tfr.mcmc(iter=5, output.dir=sim.dir)
    stopifnot(m$mcmc.list[[1]]$finished.iter == 10)
    stopifnot(get.total.iterations(m$mcmc.list, 0) == 10)
    stopifnot(m$mcmc.list[[1]]$uncertainty)
    test.ok(test.name)
    
    # summary of MCMC
    test.name <- 'summary of MCMC with uncertainty'
    start.test(test.name, wpp.year)
    sm <- summary(m, country = "Niger")
    sm2 <- summary(m, country = "Japan")
    smg <- summary(m)
    stopifnot(!sm$phase3$estimation)
    stopifnot(sm2$phase3$estimation)
    stopifnot(!is.null(smg$phase3))
    test.ok(test.name)
    
    # run MCMC for an extra country
    test.name <- 'running MCMC with uncertainty for extra country'
    start.test(test.name, wpp.year)
    countries <- c(124, 840)
    m <- run.tfr.mcmc.extra(sim.dir=sim.dir, countries = countries, iso.unbiased = countries, burnin=0, uncertainty = TRUE)
    stopifnot(sum(m$meta$regions$country_code %in% countries) == length(countries)) # countries were replaced and not added
    test.ok(test.name)
    
    test.name <- 'checking Phase III with uncertainty'
    start.test(test.name, wpp.year)
    m3 <- get.tfr3.mcmc(sim.dir=sim.dir)
    stopifnot(m3$mcmc.list[[1]]$finished.iter == 10)
    stopifnot(get.total.iterations(m3$mcmc.list, 0) == 10)
    test.ok(test.name)
    
    # run prediction
    test.name <- 'running projections with uncertainty'
    start.test(test.name, wpp.year)
    pred <- tfr.predict(m, burnin=0, burnin3=0, uncertainty = TRUE)
    spred <- summary(pred)
    stopifnot(spred$nr.traj == 10)
    test.ok(test.name)
    

    npred <- dim(pred$tfr_matrix_reconstructed)[2]
    
    # run MCMC and prediction for extra country
    test.name <- 'running projections with uncertainty for extra country'
    start.test(test.name, wpp.year)
    m <- run.tfr.mcmc.extra(sim.dir=sim.dir, countries=840, burnin=0, uncertainty = TRUE)
    # run prediction only for 840
    pred <- tfr.predict.extra(sim.dir=sim.dir, countries = 840, verbose=FALSE, uncertainty = TRUE)
    stopifnot(dim(pred$tfr_matrix_reconstructed)[2] == npred)
    stopifnot(!is.null(bayesTFR:::get.trajectories(pred, 840)$trajectories))
    stopifnot(pred$use.tfr3) 
    test.ok(test.name)

    test.name <- 'getting bias and sd models'
    start.test(test.name, wpp.year)
    bias_sd <- tfr.bias.sd(m, "Nigeria")
    stopifnot(!is.null(bias_sd))
    test.ok(test.name)
    
    test.name <- 'plotting uncertainty'
    start.test(test.name, wpp.year)
    filename <- tempfile()
    png(filename=filename)
    g <- tfr.estimation.plot(pred, "US", save.image = FALSE)
    print(g)
    dev.off()
    size <- file.info(filename)['size']
    unlink(filename)
    stopifnot(size > 0)
    test.ok(test.name)
    
    test.name <- 'write projection summary'
    start.test(test.name, wpp.year)
    write.projection.summary(sim.dir, est.uncertainty = FALSE)
    write.projection.summary(sim.dir, est.uncertainty = TRUE)
    # TODO: add a check of something
    test.ok(test.name)
    unlink(sim.dir, recursive=TRUE)
}

test.run.annual.simulation <- function(wpp.year = 2019) {
    sim.dir <- tempfile()
    
    test.name <- 'running MCMC with annual data'
    start.test(test.name, wpp.year)
    m <- run.tfr.mcmc(iter = 5, nr.chains = 1, output.dir = sim.dir, annual = TRUE, ar.phase2 = TRUE,
                      present.year = 2018, wpp.year = wpp.year)
    stopifnot(get.total.iterations(m$mcmc.list, 0) == 5)
    stopifnot(all(1953:2018 %in% rownames(m$meta$tfr_matrix)))
    stopifnot(bayesTFR:::tfr.set.identical(m, get.tfr.mcmc(sim.dir), include.output.dir=FALSE))
    stopifnot("rho_phase2" %in% tfr.parameter.names(meta = m$meta))
    test.ok(test.name)
    
    test.name <- 'running annual MCMC for extra country'
    start.test(test.name, wpp.year)
    countries <- c(900, 908)
    m <- run.tfr.mcmc.extra(sim.dir = sim.dir, countries = countries, burnin = 0)
    stopifnot(countries %in% m$meta$regions$country_code)
    test.ok(test.name)
    
    test.name <- 'running Phase III MCMC with annual data'
    start.test(test.name, wpp.year)
    m3 <- run.tfr3.mcmc(sim.dir=sim.dir, iter=20, thin=1, nr.chains=2)
    stopifnot(get.total.iterations(m3$mcmc.list, 0) == 40)
    stopifnot(bayesTFR:::tfr.set.identical(m3, get.tfr3.mcmc(sim.dir), include.output.dir=FALSE))
    test.ok(test.name)
    
    test.name <- 'running annual projections'
    start.test(test.name, wpp.year)
    pred <- tfr.predict(m, burnin=0, burnin3=0)
    spred <- summary(pred)
    stopifnot(spred$nr.traj == 5)
    stopifnot(all(2019:2100 %in% spred$projection.years))
    stopifnot(all(c(908, 900) %in% get.countries.table(pred)$code))
    test.ok(test.name)
    
    unlink(sim.dir, recursive=TRUE)
}