Running ‘test_LINGAM.R’ [1s/2s]
  Running ‘test_addBgKnowledge.R’ [2s/3s]
  Running ‘test_adjustment.R’ [3s/4s]
  Running ‘test_ages.R’ [2s/2s]
  Running ‘test_amat2dag.R’ [1s/2s]
  Running ‘test_arges.R’ [1s/2s]
  Running ‘test_backdoor.R’ [6s/7s]
  Comparing ‘test_backdoor.Rout’ to ‘test_backdoor.Rout.save’ ... OK
  Running ‘test_bicscore.R’ [2s/2s]
  Running ‘test_causalEffect.R’ [1s/2s]
  Running ‘test_coercion.R’ [2s/2s]
  Running ‘test_compareGraphs.R’ [1s/2s]
  Running ‘test_dag2cpdag.R’ [2s/2s]
  Running ‘test_dag2essgraph.R’ [2s/2s]
  Running ‘test_displayAmat.R’ [2s/2s]
  Running ‘test_dsep.R’ [2s/2s]
  Running ‘test_dsepAM.R’ [2s/2s]
  Running ‘test_dsepAMTest.R’ [2s/3s]
  Running ‘test_fci.R’ [2s/2s]
  Running ‘test_fciPlus.R’ [2s/3s]
  Running ‘test_gSquareBin.R’ [1s/2s]
  Running ‘test_gSquareDis.R’ [2s/3s]
  Running ‘test_gac.R’ [3s/5s]
  Running ‘test_getNextSet.R’ [1s/1s]
  Running ‘test_gies.R’ [3s/4s]
  Running ‘test_ida.R’ [7s/9s]
  Running ‘test_idaFast.R’ [5s/6s]
  Running ‘test_isValidGraph.R’ [3s/3s]
  Running ‘test_jointIda.R’ [2s/4s]
  Running ‘test_mat2targets.R’ [2s/2s]
  Running ‘test_optAdjSet.R’ [2s/3s]
  Running ‘test_opttarget.R’ [1s/1s]
  Running ‘test_pag2anc.R’ [1s/2s]
  Running ‘test_pag2conf.R’ [1s/1s]
  Running ‘test_pag2edge.R’ [1s/1s]
  Running ‘test_pc.R’ [2s/2s]
  Running ‘test_pcSelect.R’ [3s/4s]
  Running ‘test_pcalg2dagitty.R’ [2s/3s]
  Running ‘test_pcorOrder.R’ [1s/2s]
  Running ‘test_pdag2allDags.R’ [1s/2s]
  Running ‘test_pdag2dag.R’ [2s/2s]
  Running ‘test_possDeAn.R’ [1s/1s]
  Running ‘test_randDAG.R’ [3s/3s]
  Running ‘test_randomDAG.R’ [2s/2s]
  Running ‘test_rfci.R’ [3s/5s]
  Running ‘test_rmvDAG.R’ [1s/2s]
  Running ‘test_searchAM.R’ [1s/1s]
  Running ‘test_shd.R’ [1s/1s]
  Running ‘test_skeleton.R’ [3s/3s]
  Running ‘test_udag2pag.R’ [1s/1s]
  Running ‘test_udag2pdag.R’ [5s/6s]
  Running ‘test_wgtMatrix.R’ [1s/2s]
Running the tests in ‘tests/test_LINGAM.R’ failed.
Complete output:
  > library(pcalg)
  > ## source("/u/kalischm/research/packages/LINGAM/R/lingamFuns.R")
  > 
  > ##--> showProc.time(), assertError(), relErrV(), ...
  > R.home(); sessionInfo() # helping package maintainers to debug ...
  [1] "/home/hornik/tmp/R.check/r-devel-gcc/Work/build"
  R Under development (unstable) (2026-07-21 r90286)
  Platform: x86_64-pc-linux-gnu
  Running under: Debian GNU/Linux forky/sid
  
  Matrix products: default
  BLAS:   /home/hornik/tmp/R.check/r-devel-gcc/Work/build/lib/libRblas.so 
  LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.1;  LAPACK version 3.12.0
  
  locale:
   [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
   [4] LC_COLLATE=C           LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
   [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
  [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
  
  time zone: Europe/Vienna
  tzcode source: system (glibc)
  
  attached base packages:
  [1] stats     graphics  grDevices utils     datasets  methods   base     
  
  other attached packages:
  [1] pcalg_2.7-12
  
  loaded via a namespace (and not attached):
   [1] graph_1.90.0        igraph_2.3.3        DEoptimR_1.2-0     
   [4] clue_0.3-68         magrittr_2.0.5      fastICA_1.2-7      
   [7] abind_1.4-8         BiocGenerics_0.58.1 pkgconfig_2.0.3    
  [10] generics_0.1.4      stats4_4.7.0        lifecycle_1.0.5    
  [13] cli_3.6.6           sfsmisc_1.1-24      ggm_2.5.2          
  [16] corpcor_1.6.10      robustbase_0.99-7   compiler_4.7.0     
  [19] RBGL_1.88.0         tools_4.7.0         cluster_2.1.8.2    
  [22] bdsmatrix_1.3-7     Rcpp_1.1.2          BiocManager_1.30.27
  [25] rlang_1.3.0        
  > .libPaths()
  [1] "/home/hornik/tmp/scratch/RtmpXBoM8F/RLIBS_3223746dcca6ea"
  [2] "/home/hornik/tmp/R.check/r-devel-gcc/Work/build/library" 
  > packageDescription("pcalg")
  Package: pcalg
  Version: 2.7-12
  Date: 2024-09-12
  Title: Methods for Graphical Models and Causal Inference
  Description: Functions for causal structure learning and causal
          inference using graphical models. The main algorithms for
          causal structure learning are PC (for observational data
          without hidden variables), FCI and RFCI (for observational data
          with hidden variables), and GIES (for a mix of data from
          observational studies (i.e. observational data) and data from
          experiments involving interventions (i.e. interventional data)
          without hidden variables). For causal inference the IDA
          algorithm, the Generalized Backdoor Criterion (GBC), the
          Generalized Adjustment Criterion (GAC) and some related
          functions are implemented. Functions for incorporating
          background knowledge are provided.
  Maintainer: Markus Kalisch <kalisch@stat.math.ethz.ch>
  Authors@R: c(person("Markus","Kalisch",
          email="kalisch@stat.math.ethz.ch", role=c("aut","cre")),
          person("Alain", "Hauser", role="aut"),
          person("Martin","Maechler", role="aut"), person("Diego",
          "Colombo", role="ctb"), person("Doris", "Entner", role="ctb"),
          person("Patrik", "Hoyer", role="ctb"), person("Antti",
          "Hyttinen", role="ctb"), person("Jonas", "Peters", role="ctb"),
          person("Nicoletta", "Andri", role="ctb"), person("Emilija",
          "Perkovic", role="ctb"), person("Preetam", "Nandy",
          role="ctb"), person("Philipp", "Ruetimann",
          role="ctb"),person("Daniel", "Stekhoven", role="ctb"),
          person("Manuel", "Schuerch", role="ctb"), person("Marco",
          "Eigenmann", role="ctb"), person("Leonard", "Henckel", role =
          "ctb"),person("Joris", "Mooij", role = "ctb"))
  Depends: R (>= 3.5.0)
  LinkingTo: Rcpp (>= 0.11.0), RcppArmadillo, BH
  Imports: stats, graphics, utils, methods, abind, graph, RBGL, igraph,
          ggm, corpcor, robustbase, vcd, Rcpp, bdsmatrix, sfsmisc (>=
          1.0-26), fastICA, clue
  Suggests: MASS, Matrix, Rgraphviz, mvtnorm, huge, ggplot2, dagitty
  NeedsCompilation: yes
  Encoding: UTF-8
  License: GPL (>= 2)
  URL: https://pcalg.r-forge.r-project.org/
  Packaged: 2024-09-12 08:25:27 UTC; kalischm
  Author: Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler
          [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer
          [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta
          Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb],
          Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel
          Schuerch [ctb], Marco Eigenmann [ctb], Leonard Henckel [ctb],
          Joris Mooij [ctb]
  Repository: CRAN
  Date/Publication: 2024-09-12 17:40:27 UTC
  Built: R 4.7.0; x86_64-pc-linux-gnu; 2026-07-22 09:44:39 UTC; unix
  
  -- File: /home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/pcalg/Meta/package.rds 
  > packageDescription("Matrix")
  Package: Matrix
  Version: 1.7-5
  VersionNote: do also bump src/version.h, inst/include/Matrix/version.h
  Date: 2026-03-20
  Priority: recommended
  Title: Sparse and Dense Matrix Classes and Methods
  Description: A rich hierarchy of sparse and dense matrix classes,
          including general, symmetric, triangular, and diagonal matrices
          with numeric, logical, or pattern entries.  Efficient methods
          for operating on such matrices, often wrapping the 'BLAS',
          'LAPACK', and 'SuiteSparse' libraries.
  License: GPL (>= 2) | file LICENCE
  URL: https://Matrix.R-forge.R-project.org
  BugReports: https://R-forge.R-project.org/tracker/?atid=294&group_id=61
  Contact: Matrix-authors@R-project.org
  Authors@R: c(person("Douglas", "Bates", role = "aut", comment = c(ORCID
          = "0000-0001-8316-9503")), person("Martin", "Maechler", role =
          c("aut", "cre"), email = "mmaechler+Matrix@gmail.com", comment
          = c(ORCID = "0000-0002-8685-9910")), person("Mikael", "Jagan",
          role = "aut", comment = c(ORCID = "0000-0002-3542-2938")),
          person("Timothy A.", "Davis", role = "ctb", comment = c(ORCID =
          "0000-0001-7614-6899", "SuiteSparse libraries", "collaborators
          listed in dir(system.file(\"doc\", \"SuiteSparse\",
          package=\"Matrix\"), pattern=\"License\", full.names=TRUE,
          recursive=TRUE)")), person("George", "Karypis", role = "ctb",
          comment = c(ORCID = "0000-0003-2753-1437", "METIS library",
          "Copyright: Regents of the University of Minnesota")),
          person("Jason", "Riedy", role = "ctb", comment = c(ORCID =
          "0000-0002-4345-4200", "GNU Octave's condest() and
          onenormest()", "Copyright: Regents of the University of
          California")), person("Jens", "Oehlschlägel", role = "ctb",
          comment = "initial nearPD()"), person("R Core Team", role =
          "ctb", comment = c(ROR = "02zz1nj61", "base R's matrix
          implementation")))
  Depends: R (>= 4.4), methods
  Imports: grDevices, graphics, grid, lattice, stats, utils
  Suggests: MASS, datasets, sfsmisc, tools
  Enhances: SparseM, graph
  LazyData: no
  LazyDataNote: not possible, since we use data/*.R and our S4 classes
  BuildResaveData: no
  Encoding: UTF-8
  NeedsCompilation: yes
  Packaged: 2026-03-20 19:40:14 UTC; maechler
  Author: Douglas Bates [aut] (ORCID:
          <https://orcid.org/0000-0001-8316-9503>), Martin Maechler [aut,
          cre] (ORCID: <https://orcid.org/0000-0002-8685-9910>), Mikael
          Jagan [aut] (ORCID: <https://orcid.org/0000-0002-3542-2938>),
          Timothy A. Davis [ctb] (ORCID:
          <https://orcid.org/0000-0001-7614-6899>, SuiteSparse libraries,
          collaborators listed in dir(system.file("doc", "SuiteSparse",
          package="Matrix"), pattern="License", full.names=TRUE,
          recursive=TRUE)), George Karypis [ctb] (ORCID:
          <https://orcid.org/0000-0003-2753-1437>, METIS library,
          Copyright: Regents of the University of Minnesota), Jason Riedy
          [ctb] (ORCID: <https://orcid.org/0000-0002-4345-4200>, GNU
          Octave's condest() and onenormest(), Copyright: Regents of the
          University of California), Jens Oehlschlägel [ctb] (initial
          nearPD()), R Core Team [ctb] (ROR: <https://ror.org/02zz1nj61>,
          base R's matrix implementation)
  Maintainer: Martin Maechler <mmaechler+Matrix@gmail.com>
  Repository: CRAN
  Date/Publication: 2026-03-21 14:40:03 UTC
  Built: R 4.7.0; x86_64-pc-linux-gnu; 2026-07-22 08:56:15 UTC; unix
  
  -- File: /home/hornik/tmp/scratch/RtmpXBoM8F/RLIBS_3223746dcca6ea/Matrix/Meta/package.rds 
  > cat("doExtras:", (doExtras <- pcalg:::doExtras()), "\n")
  doExtras: FALSE 
  > 
  > 
  > ##################################################
  > ## Exp 1
  > ##################################################
  > set.seed(123)
  > n <- 500
  > eps1 <- sign(rnorm(n)) * sqrt(abs(rnorm(n)))
  > eps2 <- runif(n) - 0.5 #  ~ U[-1/2, 1/2]
  > 
  > X <- cbind(A = eps1 + 0.9*eps2,
  +            B =            eps2)
  > 
  > ## x1 <- x2
  > ## adjacency matrix:
  > ## 0 0
  > ## 1 0
  > (trueDAG <- rbind(c(0,0),
  +                   c(1,0)))
       [,1] [,2]
  [1,]    0    0
  [2,]    1    0
  > 
  > estDAG <- LINGAM(X, verbose = TRUE)
  Performing row permutation, nzdiag*() ...
    (Small dimensionality, using brute-force method.): Done!
  Performing permutation for causal order...
    (Small dimensionality, using brute-force method.): Done!
            [,1]        [,2]
  [1,] 0.0000000 0.001722255
  [2,] 0.8506492 0.000000000
  Causal B nicely triangular. No problems to report here.
  Pruning the network connections...
  Done!
  Warning message:
  In LINGAM(X, verbose = TRUE) :*** buffer overflow detected ***: terminated
  Aborted
Running the tests in ‘tests/test_udag2pdag.R’ failed.
Complete output:
  > library(pcalg)
  > suppressWarnings(RNGversion("3.5.0"))
  > .libPaths()
  [1] "/home/hornik/tmp/scratch/RtmpXBoM8F/RLIBS_3223746dcca6ea"
  [2] "/home/hornik/tmp/R.check/r-devel-gcc/Work/build/library" 
  > ## acyclic graphs
  > 
  > nreps <- 30
  > p <- 8
  > n <- 1000
  > 
  > for(u2pd in c("rand", "retry", "relaxed")) {
  +     cat("\n u2pd =", u2pd, "\n ------------\n")
  +     cyc.res <- logical(nreps)
  +     for (i in 1:nreps) {
  +         set.seed(i)
  +         myDAG <- randomDAG(p, prob = 0.2)
  +         d.mat <- rmvDAG(n, myDAG, errDist = "normal")
  +         res <- suppressWarnings(pcAlgo(d.mat, alpha = 0.05, directed=TRUE, u2pd = u2pd))
  +         ##                      ------ directed;  u2pd = "rand" --> udag2pdag()
  +         res.A <- wgtMatrix(res@graph)
  +         res.A[res.A!=0] <- 1
  +         undir.A <- res.A + t(res.A)
  +         undir.A[undir.A==1] <- 0
  +         undir.A[undir.A==2] <- 1
  +         res.dir <- res.A - undir.A
  +         cyc.res[i] <- ggm::isAcyclic(res.dir)
  +     }
  +     if (!all(cyc.res)) stop("Test of pcAlgo(*, directed): Cyclic part in PDAG!")
  + } ## for(u2pd ...)
  
   u2pd = rand 
   ------------
  
   u2pd = retry 
   ------------
  
   u2pd = relaxed 
   ------------
  > cat('Time elapsed: ', (.pt <- proc.time()),"\n")
  Time elapsed:  3.572 0.264 4.828 0.007 0.006 
  > 
  > ## find collider correctly
  > set.seed(123)
  > myDAG <- randomDAG(3, prob = 0.5)
  > library(Matrix)
  > as(myDAG,"sparseMatrix")
  3 x 3 sparse Matrix of class "dgCMatrix"
    1 2         3
  1 . . 0.4680792
  2 . . 0.9464206
  3 . . .        
  > d.mat <- rmvDAG(n, myDAG, errDist = "normal")
  > res <- pcAlgo(d.mat, alpha = 0.05, corMethod = "standard",directed=TRUE)
  Warning message:
  In pcAlgo(d.mat, alpha = 0.05, corMethod = "standard", directed = TRUE) :*** buffer overflow detected ***: terminated
  Aborted