R Under development (unstable) (2024-02-05 r85863 ucrt) -- "Unsuffered Consequences"
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> library(pcalg)
> suppressWarnings(RNGversion("3.5.0"))
> ##################################################
> ## pcAlgo object
> ##################################################
> ## Load predefined data
> data(gmG)
> n <- nrow    (gmG8$x)
> V <- colnames(gmG8$x)
> 
> ## define sufficient statistics
> suffStat <- list(C = cor(gmG8$x), n = n)
> ## estimate CPDAG
> skel.fit <- skeleton(suffStat, indepTest = gaussCItest,
+                      alpha = 0.01, labels = V)
> (amSkel <- as(skel.fit, "amat"))
Adjacency Matrix 'amat' (8 x 8) of type 'cpdag':
       Author Bar Ctrl Goal V5 V6 V7 V8
Author      .   1    .    .  .  1  .  1
Bar         1   .    1    .  1  .  .  .
Ctrl        .   1    .    .  .  .  .  .
Goal        .   .    .    .  .  .  .  .
V5          .   1    .    .  .  1  .  1
V6          1   .    .    .  1  .  1  .
V7          .   .    .    .  .  1  .  .
V8          1   .    .    .  1  .  .  .
> str(amSkel)
 'amat' num [1:8, 1:8] 0 1 0 0 0 1 0 1 1 0 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:8] "Author" "Bar" "Ctrl" "Goal" ...
  ..$ : chr [1:8] "Author" "Bar" "Ctrl" "Goal" ...
 - attr(*, "type")= chr "cpdag"
> stopifnot(attr(amSkel, "type") == "cpdag",
+           amSkel["Author", "Bar"] == 1,
+           amSkel["Bar", "Author"] == 1,
+           amSkel["Ctrl","Author"] == 0)
> 
> pc.fit <- pc(suffStat, indepTest = gaussCItest,
+              alpha = 0.01, labels = V)
> (amPC <- as(pc.fit, "amat"))
Adjacency Matrix 'amat' (8 x 8) of type 'cpdag':
       Author Bar Ctrl Goal V5 V6 V7 V8
Author      .   1    .    .  .  .  .  .
Bar         1   .    1    .  .  .  .  .
Ctrl        .   1    .    .  .  .  .  .
Goal        .   .    .    .  .  .  .  .
V5          .   1    .    .  .  .  .  .
V6          1   .    .    .  1  .  .  .
V7          .   .    .    .  .  1  .  .
V8          1   .    .    .  1  .  .  .
> stopifnot(attr(amPC, "type") == "cpdag",
+           amPC["V5", "V8"] == 0,
+           amPC["V8", "V5"] == 1,
+           amPC["Goal","Author"] == 0)
> 
> ##################################################
> ## fciAlgo object
> ##################################################
> set.seed(42)
> p <- 7
> ## generate and draw random DAG :
> myDAG <- randomDAG(p, prob = 0.4)
> 
> ## find PAG using the FCI algorithm
> myC <- cov2cor(trueCov(myDAG))
> suffStat <- list(C = myC, n = 10^9)
> V <- LETTERS[1:p] ## labels of nodes
> 
> fmFCI <- fci(suffStat, indepTest=gaussCItest, labels = V,
+              alpha = 0.9999, doPdsep = FALSE)
> (amFCI <- as(fmFCI, "amat"))
Adjacency Matrix 'amat' (7 x 7) of type 'pag':
  A B C D E F G
A . . 1 . 2 2 2
B . . . . 2 2 2
C 1 . . 1 2 2 2
D . . 1 . . 2 .
E 1 1 1 . . . 2
F 1 1 1 1 . . 2
G 3 3 3 . 1 3 .
> stopifnot(attr(amFCI, "type") == "pag",
+           amFCI["B","E"] == 2,
+           amFCI["C","D"] == 1,
+           amFCI["G","A"] == 3)
> 
> proc.time()
   user  system elapsed 
   1.20    0.25    1.39