CRAN Package Check Results for Package clusternor

Last updated on 2019-03-26 07:48:41 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.0-1 72.69 28.49 101.18 NOTE
r-devel-linux-x86_64-debian-gcc 0.0-1 56.34 24.04 80.38 NOTE
r-devel-linux-x86_64-fedora-clang 0.0-2 187.92 NOTE
r-devel-linux-x86_64-fedora-gcc 0.0-2 141.30 NOTE
r-devel-windows-ix86+x86_64 0.0-1 228.00 79.00 307.00 NOTE
r-patched-linux-x86_64 0.0-1 67.86 25.19 93.05 NOTE
r-patched-solaris-x86 0.0-2 116.60 ERROR
r-release-linux-x86_64 0.0-1 67.41 24.85 92.26 NOTE
r-release-windows-ix86+x86_64 0.0-1 222.00 64.00 286.00 NOTE
r-release-osx-x86_64 0.0-1 NOTE
r-oldrel-windows-ix86+x86_64 0.0-1 178.00 65.00 243.00 NOTE
r-oldrel-osx-x86_64 0.0-1 NOTE

Check Details

Version: 0.0-1
Check: for GNU extensions in Makefiles
Result: NOTE
    GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 0.0-2
Check: installed package size
Result: NOTE
     installed size is 6.3Mb
     sub-directories of 1Mb or more:
     libs 6.1Mb
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.0-2
Check: for GNU extensions in Makefiles
Result: NOTE
    GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86

Version: 0.0-2
Check: examples
Result: ERROR
    Running examples in ‘clusternor-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: Kmedoids
    > ### Title: Perform k-medoids clustering on a data matrix. After
    > ### initialization the k-medoids algorithm partitions data by testing
    > ### which data member of a cluster Ci may make a better candidate as
    > ### medoid (centroid) by reducing the sum of distance (usually taxi),
    > ### then running a reclustering step with updated medoids.
    > ### Aliases: Kmedoids
    >
    > ### ** Examples
    >
    > iris.mat <- as.matrix(iris[,1:4])
    > k <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes
    > km <- Kmedoids(iris.mat, k)
    
     *** caught segfault ***
    address 1a, cause 'memory not mapped'
Flavor: r-patched-solaris-x86

Version: 0.0-2
Check: tests
Result: ERROR
     Running ‘testthat.R’
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(clusternor)
     Loading required package: Rcpp
     >
     > test_check("clusternor")
    
    
     ***Running test for fcmeans***
    
     Data ==> memory, centroids ==> memory
    
     Data ==> memory, centroids ==> memory
    
     Data ==> memory, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 17
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [2,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [3,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [4,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [5,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [6,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [7,] 1.944158 1.873933 1.811169 1.829127 1.705402
     [8,] 1.944158 1.873933 1.811169 1.829127 1.705401
    
     $cluster
     [1] 7 3 7 7 3 7 7 7 7 3 7 3 7 3 3 7 3 7 3 7 3 7 7 3 3 7 7 3 3 3 7 3 3 7 3 7 3 3
     [39] 7 3 3 3 3 3 7 3 3 3 3 3
    
     $size
     [1] 0 0 29 0 0 0 21 0
    
     Data ==> memory, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 17
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [2,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [3,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [4,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [5,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [6,] 1.944158 1.873933 1.811169 1.829127 1.705401
     [7,] 1.944158 1.873933 1.811169 1.829127 1.705402
     [8,] 1.944158 1.873933 1.811169 1.829127 1.705401
    
     $cluster
     [1] 7 3 7 7 3 7 7 7 7 3 7 3 7 3 3 7 3 7 3 7 3 7 7 3 3 7 7 3 3 3 7 3 3 7 3 7 3 3
     [39] 7 3 3 3 3 3 7 3 3 3 3 3
    
     $size
     [1] 0 0 29 0 0 0 21 0
    
     Data ==> disk, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 21
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 1.639097 1.559699 1.690567 1.784127 1.738475
     [2,] 1.639094 1.559697 1.690577 1.784087 1.738516
     [3,] 1.639089 1.559693 1.690596 1.784007 1.738599
     [4,] 1.639107 1.559707 1.690528 1.784283 1.738314
     [5,] 1.639106 1.559706 1.690532 1.784267 1.738331
     [6,] 1.639107 1.559706 1.690529 1.784282 1.738317
     [7,] 1.639095 1.559698 1.690571 1.784108 1.738495
     [8,] 1.639136 1.559735 1.690416 1.784741 1.737839
    
     $cluster
     [1] 3 3 3 3 8 8 3 3 8 3 3 8 8 3 3 8 3 3 8 3 8 8 8 3 3 8 3 3 8 3 3 3 3 8 8 8 3 3
     [39] 8 3 8 8 3 3 8 3 8 8 3 3
    
     $size
     [1] 0 0 29 0 0 0 0 21
    
     Data ==> disk, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 21
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 1.639097 1.559699 1.690567 1.784127 1.738475
     [2,] 1.639094 1.559697 1.690577 1.784087 1.738516
     [3,] 1.639089 1.559693 1.690596 1.784007 1.738599
     [4,] 1.639107 1.559707 1.690528 1.784283 1.738314
     [5,] 1.639106 1.559706 1.690532 1.784267 1.738331
     [6,] 1.639107 1.559706 1.690529 1.784282 1.738317
     [7,] 1.639095 1.559698 1.690571 1.784108 1.738495
     [8,] 1.639136 1.559735 1.690416 1.784741 1.737839
    
     $cluster
     [1] 3 3 3 3 8 8 3 3 8 3 3 8 8 3 3 8 3 3 8 3 8 8 8 3 3 8 3 3 8 3 3 3 3 8 8 8 3 3
     [39] 8 3 8 8 3 3 8 3 8 8 3 3
    
     $size
     [1] 0 0 29 0 0 0 0 21
    
    
    
     ***Running test for hmeans***
    
     Data ==> ,centroids ==> memory
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 20
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 2.426766 2.953499 4.043335 2.244559 3.710492
     [2,] 4.122332 2.465384 3.589322 2.166748 3.575502
     [3,] 2.626000 3.584148 2.022466 2.295955 4.134119
     [4,] 1.943198 1.376050 2.189751 3.513497 4.592882
     [5,] 4.307359 2.781234 3.196755 4.311270 3.415023
     [6,] 2.795000 3.228041 4.430282 4.288907 1.526741
     [7,] 1.580627 3.095462 1.962946 4.057300 1.642651
     [8,] 2.477416 2.417114 2.200970 4.737974 2.761689
    
     $cluster
     [1] 5 1 3 2 7 8 1 1 8 1 4 7 6 1 5 5 5 1 5 2 6 5 2 3 4 5 1 2 7 3 3 4 3 7 5 8 1 1
     [39] 6 5 5 6 2 3 7 3 5 7 2 3
    
     $size
     [1] 9 6 8 3 11 4 6 3
    
     Data ==> memory, centroids ==> memory
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 20
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 2.888524 2.603497 2.066828 2.375178 4.408128
     [2,] 2.221906 3.679545 2.487253 2.001356 4.078200
     [3,] 3.363949 1.514688 3.809288 1.941361 3.547053
     [4,] 2.513951 3.081818 4.215258 1.409905 3.593325
     [5,] 1.676915 3.191770 2.080428 4.447292 2.429021
     [6,] 3.672408 1.905373 2.369369 4.077653 2.867364
     [7,] 3.815225 4.217887 1.862281 2.514782 2.702868
     [8,] 3.703079 4.045515 3.700746 3.861883 2.418166
    
     $cluster
     [1] 1 6 3 4 7 4 3 3 2 6 3 8 2 7 6 7 6 7 5 4 5 1 4 8 8 4 4 8 8 5 1 6 8 2 7 8 6 8
     [39] 1 8 7 8 5 8 2 8 6 8 5 8
    
     $size
     [1] 4 4 4 6 5 7 6 14
    
     Data ==> memory, centroids ==> memory
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 20
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 2.888524 2.603497 2.066828 2.375178 4.408128
     [2,] 2.221906 3.679545 2.487253 2.001356 4.078200
     [3,] 3.363949 1.514688 3.809288 1.941361 3.547053
     [4,] 2.513951 3.081818 4.215258 1.409905 3.593325
     [5,] 1.676915 3.191770 2.080428 4.447292 2.429021
     [6,] 3.672408 1.905373 2.369369 4.077653 2.867364
     [7,] 3.815225 4.217887 1.862281 2.514782 2.702868
     [8,] 3.703079 4.045515 3.700746 3.861883 2.418166
    
     $cluster
     [1] 1 6 3 4 7 4 3 3 2 6 3 8 2 7 6 7 6 7 5 4 5 1 4 8 8 4 4 8 8 5 1 6 8 2 7 8 6 8
     [39] 1 8 7 8 5 8 2 8 6 8 5 8
    
     $size
     [1] 4 4 4 6 5 7 6 14
    
     Data ==> memory, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 20
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 4.318027 3.274617 3.602438 4.262944 3.290726
     [2,] 4.269881 4.268848 2.425307 3.481196 2.341417
     [3,] 4.132397 1.768855 4.801009 1.373922 4.613465
     [4,] 4.193760 3.652908 4.680513 1.539610 4.934383
     [5,] 2.270267 3.412114 2.075016 3.820038 3.503621
     [6,] 2.111149 2.999140 1.868376 3.905545 1.677061
     [7,] 2.279295 3.155427 3.458205 1.953112 3.614739
     [8,] 3.067549 2.376654 2.715911 1.486537 2.405939
    
     $cluster
     [1] 1 5 3 4 2 7 8 8 7 6 7 2 7 8 1 2 1 8 6 7 6 8 7 1 7 7 7 6 1 5 7 1 1 5 2 8 6 1
     [39] 7 1 5 1 5 2 7 6 1 2 5 1
    
     $size
     [1] 12 6 1 1 6 6 12 6
    
     Data ==> memory, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 20
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 4.318027 3.274617 3.602438 4.262944 3.290726
     [2,] 4.269881 4.268848 2.425307 3.481196 2.341417
     [3,] 4.132397 1.768855 4.801009 1.373922 4.613465
     [4,] 4.193760 3.652908 4.680513 1.539610 4.934383
     [5,] 2.270267 3.412114 2.075016 3.820038 3.503621
     [6,] 2.111149 2.999140 1.868376 3.905545 1.677061
     [7,] 2.279295 3.155427 3.458205 1.953112 3.614739
     [8,] 3.067549 2.376654 2.715911 1.486537 2.405939
    
     $cluster
     [1] 1 5 3 4 2 7 8 8 7 6 7 2 7 8 1 2 1 8 6 7 6 8 7 1 7 7 7 6 1 5 7 1 1 5 2 8 6 1
     [39] 7 1 5 1 5 2 7 6 1 2 5 1
    
     $size
     [1] 12 6 1 1 6 6 12 6
    
     Data ==> test.data.bin , centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 20
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 4.558290 3.366808 4.267130 4.358562 4.008078
     [2,] 4.168830 2.707612 3.295831 3.355779 2.900720
     [3,] 2.840925 1.679445 3.518764 4.744002 1.590549
     [4,] 2.933031 1.435231 2.307501 4.381139 4.365722
     [5,] 2.483710 2.725254 3.572734 2.464197 3.162331
     [6,] 1.592081 2.303133 2.266422 4.066587 1.733709
     [7,] 2.449150 4.140533 1.550568 3.287508 2.548211
     [8,] 3.012037 3.752096 3.148389 2.154862 4.366184
    
     $cluster
     [1] 1 5 8 8 6 3 8 5 7 8 4 6 5 2 4 2 4 5 1 1 3 2 5 8 4 2 5 2 7 8 7 5 8 7 4 4 5 5
     [39] 5 1 1 3 5 5 6 7 2 6 8 7
    
     $size
     [1] 5 6 3 6 12 4 6 8
    
     Data ==> test.data.bin , centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $iters
     [1] 20
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 4.558290 3.366808 4.267130 4.358562 4.008078
     [2,] 4.168830 2.707612 3.295831 3.355779 2.900720
     [3,] 2.840925 1.679445 3.518764 4.744002 1.590549
     [4,] 2.933031 1.435231 2.307501 4.381139 4.365722
     [5,] 2.483710 2.725254 3.572734 2.464197 3.162331
     [6,] 1.592081 2.303133 2.266422 4.066587 1.733709
     [7,] 2.449150 4.140533 1.550568 3.287508 2.548211
     [8,] 3.012037 3.752096 3.148389 2.154862 4.366184
    
     $cluster
     [1] 1 5 8 8 6 3 8 5 7 8 4 6 5 2 4 2 4 5 1 1 3 2 5 8 4 2 5 2 7 8 7 5 8 7 4 4 5 5
     [39] 5 1 1 3 5 5 6 7 2 6 8 7
    
     $size
     [1] 5 6 3 6 12 4 6 8
    
    
    
     ***Running test for kmeanspp***
    
     Data ==> memory, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 4.676106 4.259068 3.930618 4.701506 1.163457
     [2,] 4.360751 3.103981 2.929963 3.557833 4.917737
     [3,] 2.577532 1.344223 1.863300 4.038939 3.975245
     [4,] 4.132397 1.768855 4.801009 1.373922 4.613465
     [5,] 4.193760 3.652908 4.680513 1.539610 4.934383
     [6,] 4.646589 4.560930 1.590640 3.080840 3.667521
     [7,] 1.790205 2.395572 4.524249 1.312929 2.989034
     [8,] 2.340891 1.256685 3.564322 1.279626 1.655872
    
     $cluster
     [1] 2 3 4 5 6 7 8 4 2 3 7 6 7 6 2 6 1 6 3 5 3 8 7 2 7 7 7 7 1 3 2 3 1 6 6 8 3 1
     [39] 3 1 6 1 6 1 7 6 2 1 3 1
    
     $size
     [1] 9 6 9 2 2 10 9 3
    
     $best.start
     [1] 5
    
     $energy
     [1] 250.3165
    
     Data ==> disk, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 1.412685 1.504301 2.981776 4.041901 4.939007
     [2,] 4.360751 2.577532 4.132397 4.193760 4.646589
     [3,] 1.790205 2.340891 4.072918 2.111099 3.215880
     [4,] 2.909588 3.515484 2.459138 3.053604 4.808919
     [5,] 4.664780 3.542847 3.869188 1.566410 3.427876
     [6,] 1.065202 1.971547 1.548926 4.216707 1.626716
     [7,] 2.603778 1.519162 1.435235 4.995698 1.873028
     [8,] 3.051730 4.356449 3.450559 2.184126 3.550209
    
     $cluster
     [1] 2 3 4 5 6 7 8 3 7 8 1 6 8 3 1 2 2 3 2 2 3 5 5 4 1 8 3 5 6 4 8 1 4 6 7 1 8 3
     [39] 8 2 2 7 5 3 6 4 2 6 5 4
    
     $size
     [1] 5 8 8 6 6 6 4 7
    
     $best.start
     [1] 9
    
     $energy
     [1] 209.9886
    
     Data ==> memory, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 4.676106 4.259068 3.930618 4.701506 1.163457
     [2,] 4.360751 3.103981 2.929963 3.557833 4.917737
     [3,] 2.577532 1.344223 1.863300 4.038939 3.975245
     [4,] 4.132397 1.768855 4.801009 1.373922 4.613465
     [5,] 4.193760 3.652908 4.680513 1.539610 4.934383
     [6,] 4.646589 4.560930 1.590640 3.080840 3.667521
     [7,] 1.790205 2.395572 4.524249 1.312929 2.989034
     [8,] 2.340891 1.256685 3.564322 1.279626 1.655872
    
     $cluster
     [1] 2 3 4 5 6 7 8 4 2 3 7 6 7 6 2 6 1 6 3 5 3 8 7 2 7 7 7 7 1 3 2 3 1 6 6 8 3 1
     [39] 3 1 6 1 6 1 7 6 2 1 3 1
    
     $size
     [1] 9 6 9 2 2 10 9 3
    
     $best.start
     [1] 5
    
     $energy
     [1] 250.3165
    
     Data ==> memory, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 4.676106 4.259068 3.930618 4.701506 1.163457
     [2,] 4.360751 3.103981 2.929963 3.557833 4.917737
     [3,] 2.577532 1.344223 1.863300 4.038939 3.975245
     [4,] 4.132397 1.768855 4.801009 1.373922 4.613465
     [5,] 4.193760 3.652908 4.680513 1.539610 4.934383
     [6,] 4.646589 4.560930 1.590640 3.080840 3.667521
     [7,] 1.790205 2.395572 4.524249 1.312929 2.989034
     [8,] 2.340891 1.256685 3.564322 1.279626 1.655872
    
     $cluster
     [1] 2 3 4 5 6 7 8 4 2 3 7 6 7 6 2 6 1 6 3 5 3 8 7 2 7 7 7 7 1 3 2 3 1 6 6 8 3 1
     [39] 3 1 6 1 6 1 7 6 2 1 3 1
    
     $size
     [1] 9 6 9 2 2 10 9 3
    
     $best.start
     [1] 5
    
     $energy
     [1] 250.3165
    
     Data ==> disk, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 1.412685 1.504301 2.981776 4.041901 4.939007
     [2,] 4.360751 2.577532 4.132397 4.193760 4.646589
     [3,] 1.790205 2.340891 4.072918 2.111099 3.215880
     [4,] 2.909588 3.515484 2.459138 3.053604 4.808919
     [5,] 4.664780 3.542847 3.869188 1.566410 3.427876
     [6,] 1.065202 1.971547 1.548926 4.216707 1.626716
     [7,] 2.603778 1.519162 1.435235 4.995698 1.873028
     [8,] 3.051730 4.356449 3.450559 2.184126 3.550209
    
     $cluster
     [1] 2 3 4 5 6 7 8 3 7 8 1 6 8 3 1 2 2 3 2 2 3 5 5 4 1 8 3 5 6 4 8 1 4 6 7 1 8 3
     [39] 8 2 2 7 5 3 6 4 2 6 5 4
    
     $size
     [1] 5 8 8 6 6 6 4 7
    
     $best.start
     [1] 9
    
     $energy
     [1] 209.9886
    
     Data ==> disk, centroids ==> compute
    
     $nrow
     [1] 50
    
     $ncol
     [1] 5
    
     $k
     [1] 8
    
     $centers
     [,1] [,2] [,3] [,4] [,5]
     [1,] 1.412685 1.504301 2.981776 4.041901 4.939007
     [2,] 4.360751 2.577532 4.132397 4.193760 4.646589
     [3,] 1.790205 2.340891 4.072918 2.111099 3.215880
     [4,] 2.909588 3.515484 2.459138 3.053604 4.808919
     [5,] 4.664780 3.542847 3.869188 1.566410 3.427876
     [6,] 1.065202 1.971547 1.548926 4.216707 1.626716
     [7,] 2.603778 1.519162 1.435235 4.995698 1.873028
     [8,] 3.051730 4.356449 3.450559 2.184126 3.550209
    
     $cluster
     [1] 2 3 4 5 6 7 8 3 7 8 1 6 8 3 1 2 2 3 2 2 3 5 5 4 1 8 3 5 6 4 8 1 4 6 7 1 8 3
     [39] 8 2 2 7 5 3 6 4 2 6 5 4
    
     $size
     [1] 5 8 8 6 6 6 4 7
    
     $best.start
     [1] 9
    
     $energy
     [1] 209.9886
    
    
    
     ***Running test for kmedoids***
    
     Data ==> memory, centroids ==> compute
    
    
     *** caught segfault ***
     address 38, cause 'memory not mapped'
    
     Traceback:
     1: Kmedoids(test_data, k, nrow, ncol, nthread = nthread)
     2: print(Kmedoids(test_data, k, nrow, ncol, nthread = nthread))
     3: test.data.in.mem()
     4: eval(code, test_env)
     5: eval(code, test_env)
     6: withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error)
     7: doTryCatch(return(expr), name, parentenv, handler)
     8: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     9: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     10: doTryCatch(return(expr), name, parentenv, handler)
     11: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])
     12: tryCatchList(expr, classes, parentenv, handlers)
     13: tryCatch(withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error), error = handle_fatal, skip = function(e) { })
     14: test_code(NULL, exprs, env)
     15: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     16: force(code)
     17: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter, { lister$start_file(basename(path)) source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap) end_context() })
     18: FUN(X[[i]], ...)
     19: lapply(paths, test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     20: force(code)
     21: with_reporter(reporter = current_reporter, results <- lapply(paths, test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap))
     22: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, wrap = wrap)
     23: test_dir(path = test_path, reporter = reporter, env = env, filter = filter, ..., stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, wrap = wrap)
     24: test_package_dir(package = package, test_path = test_path, filter = filter, reporter = reporter, ..., stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, wrap = wrap)
     25: test_check("clusternor")
     An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86