t_end <- 10
lDAG <- DAG.empty()
lDAG <- lDAG +
	node(name = "L2", t = 0, distr = "rconst", const = 0) +
	node(name = "A1", t = 0, distr = "rconst", const = 0) +
	node(name = "L2", t = 1:t_end, distr = "rbern",
 	prob = ifelse(A1[t - 1]  ==  1, 0.1,
 			ifelse(L2[t-1] == 1, 0.9,
        min(1,0.1 + t/t_end)))) +
	node(name = "A1", t = 1:t_end, distr = "rbern",
 	prob = ifelse(A1[t - 1]  ==  1, 1,
 			 ifelse(L2[0] == 0, 0.3,
			  ifelse(L2[0] == 0, 0.1,
			   ifelse(L2[0] == 1, 0.7, 0.5))))) +
	node(name = "Y", t = 1:t_end, distr = "rbern",
 	prob = plogis(-6.5 + 4 * L2[t] + 0.05 * sum(I(L2[0:t] == rep(0,(t + 1))))),
 	EFU = TRUE)
lDAG <- set.DAG(lDAG)
#---------------------------------------------------------------------------------------
# EXAMPLE 1. No forward imputation.
#---------------------------------------------------------------------------------------
Odat.wide <- sim(DAG = lDAG, n = 1000, rndseed = 123)
Odat.wide[c(21,47), 1:18]
Odat.wideLTCF <- sim(DAG = lDAG, n = 1000, LTCF = "Y", rndseed = 123)
Odat.wideLTCF[c(21,47), 1:18]
#---------------------------------------------------------------------------------------
# EXAMPLE 2. With forward imputation.
#---------------------------------------------------------------------------------------
Odat.wideLTCF2 <- doLTCF(data = Odat.wide, LTCF = "Y")
Odat.wideLTCF2[c(21,47), 1:18]
# all.equal(Odat.wideLTCF, Odat.wideLTCF2)