# To do !!!
## 1. CPDAG -> DAG
# possible to extend
test1 <- pcalg::pdag2dag(truecpdag)
test2 <- pcalg::pdag2dag(cpdag1)
# impossible to extend.
impcpdag <- as(rbind(c(0, 1, 0, 1),
c(1, 0, 1, 0),
c(0, 1, 0, 1),
c(1, 0, 1, 0)), "graphNEL")
test3 <- pcalg::pdag2dag(impcpdag)
if (test3$success){
as(test3$graph, "matrix")
}
## 2. PC algorithm
# NORMAL PC
# pc.fit <- pc(suffStat = suffStat, indepTest = gaussCItest, p = p, alpha = 5e-3,
# u2pd = "retry", skel.method = "stable")
#
# RANK PC
# suffStat.data <- list(C = 2 * sin(cor(X, method = "spearman") *
# pi/6), n = nrow(X))
# setOptions$indepTest <- pcalg::gaussCItest
# rankpc.fit <- pc(suffStat = suffStat, indepTest = gaussCItest, p = p, alpha = 5e-3,
# u2pd = "retry", skel.method = "stable")
## 3. Add references to "Causal discovery in heavy-tailed models"
## 4. Change name of "Greedy ancestral search"
## 5. Change name of causal_tail_coeff and causal_tail_matrix??
## 6. Note that with positive and negative beta, there is higher chance of an offset.
# This leads to weaker unconditional connections, which might lead to poorer perf
# in finite sample
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.