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 |
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