Running ‘test_LINGAM.R’ [3s/5s]
  Running ‘test_addBgKnowledge.R’ [2s/3s]
  Running ‘test_adjustment.R’ [4s/6s]
  Running ‘test_ages.R’ [2s/3s]
  Running ‘test_amat2dag.R’ [1s/3s]
  Running ‘test_arges.R’ [2s/2s]
  Running ‘test_backdoor.R’ [7s/10s]
  Comparing ‘test_backdoor.Rout’ to ‘test_backdoor.Rout.save’ ... OK
  Running ‘test_bicscore.R’ [2s/3s]
  Running ‘test_causalEffect.R’ [1s/2s]
  Running ‘test_coercion.R’ [2s/2s]
  Running ‘test_compareGraphs.R’ [2s/3s]
  Running ‘test_dag2cpdag.R’ [2s/3s]
  Running ‘test_dag2essgraph.R’ [2s/3s]
  Running ‘test_displayAmat.R’ [2s/3s]
  Running ‘test_dsep.R’ [2s/2s]
  Running ‘test_dsepAM.R’ [2s/3s]
  Running ‘test_dsepAMTest.R’ [2s/4s]
  Running ‘test_fci.R’ [2s/3s]
  Running ‘test_fciPlus.R’ [3s/7s]
  Running ‘test_gSquareBin.R’ [2s/2s]
  Running ‘test_gSquareDis.R’ [3s/4s]
  Running ‘test_gac.R’ [3s/5s]
  Running ‘test_getNextSet.R’ [1s/3s]
  Running ‘test_gies.R’ [5s/6s]
  Running ‘test_ida.R’ [8s/9s]
  Running ‘test_idaFast.R’ [4s/6s]
  Running ‘test_isValidGraph.R’ [3s/6s]
  Running ‘test_jointIda.R’ [2s/3s]
  Running ‘test_mat2targets.R’ [2s/3s]
  Running ‘test_optAdjSet.R’ [2s/4s]
  Running ‘test_opttarget.R’ [1s/3s]
  Running ‘test_pag2anc.R’ [1s/3s]
  Running ‘test_pag2conf.R’ [1s/3s]
  Running ‘test_pag2edge.R’ [1s/3s]
  Running ‘test_pc.R’ [2s/4s]
  Running ‘test_pcSelect.R’ [3s/5s]
  Running ‘test_pcalg2dagitty.R’ [2s/3s]
  Running ‘test_pcorOrder.R’ [1s/3s]
  Running ‘test_pdag2allDags.R’ [1s/2s]
  Running ‘test_pdag2dag.R’ [2s/3s]
  Running ‘test_possDeAn.R’ [2s/3s]
  Running ‘test_randDAG.R’ [3s/4s]
  Comparing ‘test_randDAG.Rout’ to ‘test_randDAG.Rout.save’ ... OK
  Running ‘test_randomDAG.R’ [2s/3s]
  Running ‘test_rfci.R’ [3s/5s]
  Running ‘test_rmvDAG.R’ [2s/3s]
  Running ‘test_searchAM.R’ [1s/2s]
  Running ‘test_shd.R’ [2s/2s]
  Running ‘test_skeleton.R’ [5s/8s]
  Running ‘test_udag2pag.R’ [1s/2s]
  Running ‘test_udag2pdag.R’ [5s/9s]
  Running ‘test_wgtMatrix.R’ [1s/3s]
Running the tests in ‘tests/test_rfci.R’ failed.
Complete output:
  > library(pcalg)
  > doExtras <- pcalg:::doExtras()
  > 
  > source(system.file(package="Matrix", "test-tools-1.R", mustWork=TRUE))
  Loading required package: tools
  > ##--> 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) (2021-05-24 r80372)
  Platform: x86_64-pc-linux-gnu (64-bit)
  Running under: Debian GNU/Linux 11 (bullseye)
  
  Matrix products: default
  BLAS:   /home/hornik/tmp/R.check/r-devel-gcc/Work/build/lib/libRblas.so
  LAPACK: /home/hornik/tmp/R.check/r-devel-gcc/Work/build/lib/libRlapack.so
  
  locale:
   [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
   [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
   [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
   [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
   [9] LC_ADDRESS=C               LC_TELEPHONE=C            
  [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
  
  attached base packages:
  [1] tools     stats     graphics  grDevices utils     datasets  methods  
  [8] base     
  
  other attached packages:
  [1] pcalg_2.7-2
  
  loaded via a namespace (and not attached):
   [1] bdsmatrix_1.3-4     Rcpp_1.0.6          lattice_0.20-44    
   [4] corpcor_1.6.9       grid_4.2.0          RBGL_1.68.0        
   [7] ggm_2.5             stats4_4.2.0        magrittr_2.0.1     
  [10] graph_1.70.0        robustbase_0.93-7   Matrix_1.3-3       
  [13] fastICA_1.2-2       igraph_1.2.6        DEoptimR_1.0-9     
  [16] sfsmisc_1.1-11      abind_1.4-5         parallel_4.2.0     
  [19] compiler_4.2.0      clue_0.3-59         pkgconfig_2.0.3    
  [22] BiocGenerics_0.38.0 cluster_2.1.2      
  > .libPaths()
  [1] "/home/hornik/tmp/scratch/RtmpHXoIO6/RLIBS_5483295b2b88" 
  [2] "/home/hornik/tmp/R.check/r-devel-gcc/Work/build/library"
  > packageDescription("pcalg")
  Package: pcalg
  Version: 2.7-2
  Date: 2021-4-20
  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"))
  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]
  Depends: R (>= 3.0.2)
  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
  ByteCompile: yes
  NeedsCompilation: yes
  Encoding: UTF-8
  License: GPL (>= 2)
  URL: http://pcalg.r-forge.r-project.org/
  RoxygenNote: 6.1.1
  Packaged: 2021-04-20 09:33:22 UTC; kalischm
  Repository: CRAN
  Date/Publication: 2021-04-22 04:50:13 UTC
  Built: R 4.2.0; x86_64-pc-linux-gnu; 2021-05-25 06:06:40 UTC; unix
  
  -- File: /home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/pcalg/Meta/package.rds 
  > packageDescription("Matrix")
  Package: Matrix
  Version: 1.3-3
  Date: 2021-05-01
  Priority: recommended
  Title: Sparse and Dense Matrix Classes and Methods
  Contact: Doug and Martin <Matrix-authors@R-project.org>
  Maintainer: Martin Maechler <mmaechler+Matrix@gmail.com>
  Authors@R: c(person("Douglas","Bates", role="aut") ,
          person("Martin","Maechler", role = c("aut","cre"),
          email="mmaechler+Matrix@gmail.com", comment = c(ORCID =
          "0000-0002-8685-9910")) , person("Timothy A.", "Davis",
          role="ctb", comment = c("SuiteSparse and 'cs' C libraries,
          notably CHOLMOD, AMD; collaborators listed in dir(pattern =
          '^[A-Z]+[.]txt$', full.names=TRUE, system.file('doc',
          'SuiteSparse', package='Matrix'))")) , person("Jens",
          "Oehlschlägel", role="ctb", comment="initial nearPD()") ,
          person("Jason", "Riedy", role="ctb", comment = c("condest() and
          onenormest() for octave", "Copyright: Regents of the University
          of California")) , person("R Core Team", role = "ctb",
          comment="base R matrix implementation") )
  Description: A rich hierarchy of matrix classes, including triangular,
          symmetric, and diagonal matrices, both dense and sparse and
          with pattern, logical and numeric entries.  Numerous methods
          for and operations on these matrices, using 'LAPACK' and
          'SuiteSparse' libraries.
  Depends: R (>= 3.5.0)
  Imports: methods, graphics, grid, stats, utils, lattice
  Suggests: expm, MASS
  Enhances: MatrixModels, graph, SparseM, sfsmisc, igraph, maptools, sp,
          spdep
  EnhancesNote: line 2: for "Rd xrefs"
  Encoding: UTF-8
  LazyData: no
  LazyDataNote: not possible, since we use data/*.R *and* our classes
  BuildResaveData: no
  License: GPL (>= 2) | file LICENCE
  URL: http://Matrix.R-forge.R-project.org/
  BugReports: https://r-forge.r-project.org/tracker/?group_id=61
  NeedsCompilation: yes
  Packaged: 2021-05-01 13:24:41 UTC; maechler
  Author: Douglas Bates [aut], Martin Maechler [aut, cre]
          (<https://orcid.org/0000-0002-8685-9910>), Timothy A. Davis
          [ctb] (SuiteSparse and 'cs' C libraries, notably CHOLMOD, AMD;
          collaborators listed in dir(pattern = '^[A-Z]+[.]txt$',
          full.names=TRUE, system.file('doc', 'SuiteSparse',
          package='Matrix'))), Jens Oehlschlägel [ctb] (initial
          nearPD()), Jason Riedy [ctb] (condest() and onenormest() for
          octave, Copyright: Regents of the University of California), R
          Core Team [ctb] (base R matrix implementation)
  Repository: CRAN
  Date/Publication: 2021-05-04 10:50:02 UTC
  Built: R 4.2.0; x86_64-pc-linux-gnu; 2021-05-25 05:08:37 UTC; unix
  
  -- File: /home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/Matrix/Meta/package.rds 
  > 
  > ## load the functions for the simulations of this paper
  > ## source("/u/colombo/Diss/RAusw/First_paper_RFCI/functions_for_the_simulations.R")
  > 
  > ## RFCI improves the output
  > ##______________________________________________
  > ## Input:  L1=1; L2=2; X1=6; X2=4; X3=3; X4=5; X5=7; X6=8
  > ## Output: X1=4; X2=2; X3=1; X4=3; X5=5; X6=6
  > 
  > amat <- t(matrix(c(0,0,0,0,0,0,0,0, 0,0,0,0,1,1,0,0,
  +                    0,1,0,1,0,1,0,0, 1,0,0,0,0,1,0,0,
  +                    0,0,0,0,0,1,0,0, 1,1,0,0,0,0,0,0,
  +                    0,0,0,1,1,0,0,0), 8,8))
  Error in matrix(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,  : 
    data length differs from size of matrix: [56 != 8 x 8]
  Calls: t -> matrix
  Execution halted