• using R version 3.6.3 (2020-02-29)
• using platform: x86_64-w64-mingw32 (64-bit)
• using session charset: ISO8859-1
• checking for file 'MixAll/DESCRIPTION' ... OK
• this is package 'MixAll' version '1.5.1'
• package encoding: UTF-8
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking if there is a namespace ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking whether package 'MixAll' can be installed ... OK
• checking installed package size ... NOTE
    installed size is 6.8Mb
    sub-directories of 1Mb or more:
      libs 4.7Mb
• checking package directory ... OK
• checking 'build' directory ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking for left-over files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking R files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• loading checks for arch 'i386'
  • checking whether the package can be loaded ... OK
  • checking whether the package can be loaded with stated dependencies ... OK
  • checking whether the package can be unloaded cleanly ... OK
  • checking whether the namespace can be loaded with stated dependencies ... OK
  • checking whether the namespace can be unloaded cleanly ... OK
  • checking loading without being on the library search path ... OK
  • checking use of S3 registration ... OK
• loading checks for arch 'x64'
  • checking whether the package can be loaded ... OK
  • checking whether the package can be loaded with stated dependencies ... OK
  • checking whether the package can be unloaded cleanly ... OK
  • checking whether the namespace can be loaded with stated dependencies ... OK
  • checking whether the namespace can be unloaded cleanly ... OK
  • checking loading without being on the library search path ... OK
  • checking use of S3 registration ... OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... [10s] OK
• checking Rd files ... OK
• checking Rd metadata ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking for unstated dependencies in examples ... OK
• checking contents of 'data' directory ... OK
• checking data for non-ASCII characters ... OK
• checking data for ASCII and uncompressed saves ... OK
• checking line endings in shell scripts ... OK
• checking line endings in C/C++/Fortran sources/headers ... OK
• checking line endings in Makefiles ... OK
• checking compilation flags in Makevars ... OK
• checking for GNU extensions in Makefiles ... OK
• checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
• checking include directives in Makefiles ... OK
• checking pragmas in C/C++ headers and code ... OK
• checking compiled code ... OK
• checking sizes of PDF files under 'inst/doc' ... OK
• checking installed files from 'inst/doc' ... OK
• checking files in 'vignettes' ... OK
• checking examples ...
  • running examples for arch 'i386' ... [15s] OK
  • running examples for arch 'x64' ... [14s] OK
• checking for unstated dependencies in 'tests' ... OK
• checking tests ...
  • running tests for arch 'i386' ... [18s] OK
      Running 'ClusterSimul.R' [0s]
      Running 'clusterDiagGaussianLikelihood.R' [1s]
      Running 'clusterGammaLikelihood.R' [1s]
      Running 'simulHeterogeneous.R' [0s]
      Running 'simulNonLinear.R' [1s]
      Running 'testAllLearners.R' [2s]
      Running 'testPoissonExample.R' [2s]
      Running 'testPredict.R' [10s]
  • running tests for arch 'x64' ... [17s] ERROR
      Running 'ClusterSimul.R' [0s]
      Running 'clusterDiagGaussianLikelihood.R' [1s]
      Running 'clusterGammaLikelihood.R' [1s]
      Running 'simulHeterogeneous.R' [0s]
      Running 'simulNonLinear.R' [1s]
      Running 'testAllLearners.R' [1s]
      Running 'testPoissonExample.R' [2s]
      Running 'testPredict.R' [10s]
    Running the tests in 'tests/testAllLearners.R' failed.
    Complete output:
      > library(MixAll)
      Loading required package: rtkore
      Loading required package: Rcpp
      
      Attaching package: 'rtkore'
      
      The following object is masked from 'package:Rcpp':
      
          LdFlags
      
      > ## get data and target from iris data set
      > data(iris)
      > x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x)
      > ## add missing values at random
      > indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2)
      > cbind(indexes, x[indexes])
           [,1] [,2] [,3]
      [1,] 1 3 1.4
      [2,] 59 3 4.6
      [3,] 22 2 3.7
      [4,] 12 3 1.6
      [5,] 11 4 0.2
      > x[indexes] <- NA
      > ## learn continuous model
      > model <- learnDiagGaussian( data=x, labels= z, prop = c(1/3,1/3,1/3)
      + , models = clusterDiagGaussianNames(prop = "equal")
      + , algo = "simul", nbIter = 2, epsilon = 1e-08
      + )
      > missingValues(model)
        row col value
      1 22 2 2.83124151
      2 1 3 1.29522460
      3 12 3 1.08062155
      4 59 3 4.51286262
      5 11 4 -0.06608789
      > print(model)
      ****************************************
      * model name = gaussian_p_sj
      * data =
             Sepal.Length Sepal.Width Petal.Length Petal.Width
        [1,] 5.10000000 3.50000000 1.29522460 0.20000000
        [2,] 4.90000000 3.00000000 1.40000000 0.20000000
        [3,] 4.70000000 3.20000000 1.30000000 0.20000000
        [4,] 4.60000000 3.10000000 1.50000000 0.20000000
        [5,] 5.00000000 3.60000000 1.40000000 0.20000000
        [6,] 5.40000000 3.90000000 1.70000000 0.40000000
        [7,] 4.60000000 3.40000000 1.40000000 0.30000000
        [8,] 5.00000000 3.40000000 1.50000000 0.20000000
        [9,] 4.40000000 2.90000000 1.40000000 0.20000000
       [10,] 4.90000000 3.10000000 1.50000000 0.10000000
       [11,] 5.40000000 3.70000000 1.50000000 -0.06608789
       [12,] 4.80000000 3.40000000 1.08062155 0.20000000
       [13,] 4.80000000 3.00000000 1.40000000 0.10000000
       [14,] 4.30000000 3.00000000 1.10000000 0.10000000
       [15,] 5.80000000 4.00000000 1.20000000 0.20000000
       [16,] 5.70000000 4.40000000 1.50000000 0.40000000
       [17,] 5.40000000 3.90000000 1.30000000 0.40000000
       [18,] 5.10000000 3.50000000 1.40000000 0.30000000
       [19,] 5.70000000 3.80000000 1.70000000 0.30000000
       [20,] 5.10000000 3.80000000 1.50000000 0.30000000
       [21,] 5.40000000 3.40000000 1.70000000 0.20000000
       [22,] 5.10000000 2.83124151 1.50000000 0.40000000
       [23,] 4.60000000 3.60000000 1.00000000 0.20000000
       [24,] 5.10000000 3.30000000 1.70000000 0.50000000
       [25,] 4.80000000 3.40000000 1.90000000 0.20000000
       [26,] 5.00000000 3.00000000 1.60000000 0.20000000
       [27,] 5.00000000 3.40000000 1.60000000 0.40000000
       [28,] 5.20000000 3.50000000 1.50000000 0.20000000
       [29,] 5.20000000 3.40000000 1.40000000 0.20000000
       [30,] 4.70000000 3.20000000 1.60000000 0.20000000
       [31,] 4.80000000 3.10000000 1.60000000 0.20000000
       [32,] 5.40000000 3.40000000 1.50000000 0.40000000
       [33,] 5.20000000 4.10000000 1.50000000 0.10000000
       [34,] 5.50000000 4.20000000 1.40000000 0.20000000
       [35,] 4.90000000 3.10000000 1.50000000 0.20000000
       [36,] 5.00000000 3.20000000 1.20000000 0.20000000
       [37,] 5.50000000 3.50000000 1.30000000 0.20000000
       [38,] 4.90000000 3.60000000 1.40000000 0.10000000
       [39,] 4.40000000 3.00000000 1.30000000 0.20000000
       [40,] 5.10000000 3.40000000 1.50000000 0.20000000
       [41,] 5.00000000 3.50000000 1.30000000 0.30000000
       [42,] 4.50000000 2.30000000 1.30000000 0.30000000
       [43,] 4.40000000 3.20000000 1.30000000 0.20000000
       [44,] 5.00000000 3.50000000 1.60000000 0.60000000
       [45,] 5.10000000 3.80000000 1.90000000 0.40000000
       [46,] 4.80000000 3.00000000 1.40000000 0.30000000
       [47,] 5.10000000 3.80000000 1.60000000 0.20000000
       [48,] 4.60000000 3.20000000 1.40000000 0.20000000
       [49,] 5.30000000 3.70000000 1.50000000 0.20000000
       [50,] 5.00000000 3.30000000 1.40000000 0.20000000
       [51,] 7.00000000 3.20000000 4.70000000 1.40000000
       [52,] 6.40000000 3.20000000 4.50000000 1.50000000
       [53,] 6.90000000 3.10000000 4.90000000 1.50000000
       [54,] 5.50000000 2.30000000 4.00000000 1.30000000
       [55,] 6.50000000 2.80000000 4.60000000 1.50000000
       [56,] 5.70000000 2.80000000 4.50000000 1.30000000
       [57,] 6.30000000 3.30000000 4.70000000 1.60000000
       [58,] 4.90000000 2.40000000 3.30000000 1.00000000
       [59,] 6.60000000 2.90000000 4.51286262 1.30000000
       [60,] 5.20000000 2.70000000 3.90000000 1.40000000
       [61,] 5.00000000 2.00000000 3.50000000 1.00000000
       [62,] 5.90000000 3.00000000 4.20000000 1.50000000
       [63,] 6.00000000 2.20000000 4.00000000 1.00000000
       [64,] 6.10000000 2.90000000 4.70000000 1.40000000
       [65,] 5.60000000 2.90000000 3.60000000 1.30000000
       [66,] 6.70000000 3.10000000 4.40000000 1.40000000
       [67,] 5.60000000 3.00000000 4.50000000 1.50000000
       [68,] 5.80000000 2.70000000 4.10000000 1.00000000
       [69,] 6.20000000 2.20000000 4.50000000 1.50000000
       [70,] 5.60000000 2.50000000 3.90000000 1.10000000
       [71,] 5.90000000 3.20000000 4.80000000 1.80000000
       [72,] 6.10000000 2.80000000 4.00000000 1.30000000
       [73,] 6.30000000 2.50000000 4.90000000 1.50000000
       [74,] 6.10000000 2.80000000 4.70000000 1.20000000
       [75,] 6.40000000 2.90000000 4.30000000 1.30000000
       [76,] 6.60000000 3.00000000 4.40000000 1.40000000
       [77,] 6.80000000 2.80000000 4.80000000 1.40000000
       [78,] 6.70000000 3.00000000 5.00000000 1.70000000
       [79,] 6.00000000 2.90000000 4.50000000 1.50000000
       [80,] 5.70000000 2.60000000 3.50000000 1.00000000
       [81,] 5.50000000 2.40000000 3.80000000 1.10000000
       [82,] 5.50000000 2.40000000 3.70000000 1.00000000
       [83,] 5.80000000 2.70000000 3.90000000 1.20000000
       [84,] 6.00000000 2.70000000 5.10000000 1.60000000
       [85,] 5.40000000 3.00000000 4.50000000 1.50000000
       [86,] 6.00000000 3.40000000 4.50000000 1.60000000
       [87,] 6.70000000 3.10000000 4.70000000 1.50000000
       [88,] 6.30000000 2.30000000 4.40000000 1.30000000
       [89,] 5.60000000 3.00000000 4.10000000 1.30000000
       [90,] 5.50000000 2.50000000 4.00000000 1.30000000
       [91,] 5.50000000 2.60000000 4.40000000 1.20000000
       [92,] 6.10000000 3.00000000 4.60000000 1.40000000
       [93,] 5.80000000 2.60000000 4.00000000 1.20000000
       [94,] 5.00000000 2.30000000 3.30000000 1.00000000
       [95,] 5.60000000 2.70000000 4.20000000 1.30000000
       [96,] 5.70000000 3.00000000 4.20000000 1.20000000
       [97,] 5.70000000 2.90000000 4.20000000 1.30000000
       [98,] 6.20000000 2.90000000 4.30000000 1.30000000
       [99,] 5.10000000 2.50000000 3.00000000 1.10000000
      [100,] 5.70000000 2.80000000 4.10000000 1.30000000
      [101,] 6.30000000 3.30000000 6.00000000 2.50000000
      [102,] 5.80000000 2.70000000 5.10000000 1.90000000
      [103,] 7.10000000 3.00000000 5.90000000 2.10000000
      [104,] 6.30000000 2.90000000 5.60000000 1.80000000
      [105,] 6.50000000 3.00000000 5.80000000 2.20000000
      [106,] 7.60000000 3.00000000 6.60000000 2.10000000
      [107,] 4.90000000 2.50000000 4.50000000 1.70000000
      [108,] 7.30000000 2.90000000 6.30000000 1.80000000
      [109,] 6.70000000 2.50000000 5.80000000 1.80000000
      [110,] 7.20000000 3.60000000 6.10000000 2.50000000
      [111,] 6.50000000 3.20000000 5.10000000 2.00000000
      [112,] 6.40000000 2.70000000 5.30000000 1.90000000
      [113,] 6.80000000 3.00000000 5.50000000 2.10000000
      [114,] 5.70000000 2.50000000 5.00000000 2.00000000
      [115,] 5.80000000 2.80000000 5.10000000 2.40000000
      [116,] 6.40000000 3.20000000 5.30000000 2.30000000
      [117,] 6.50000000 3.00000000 5.50000000 1.80000000
      [118,] 7.70000000 3.80000000 6.70000000 2.20000000
      [119,] 7.70000000 2.60000000 6.90000000 2.30000000
      [120,] 6.00000000 2.20000000 5.00000000 1.50000000
      [121,] 6.90000000 3.20000000 5.70000000 2.30000000
      [122,] 5.60000000 2.80000000 4.90000000 2.00000000
      [123,] 7.70000000 2.80000000 6.70000000 2.00000000
      [124,] 6.30000000 2.70000000 4.90000000 1.80000000
      [125,] 6.70000000 3.30000000 5.70000000 2.10000000
      [126,] 7.20000000 3.20000000 6.00000000 1.80000000
      [127,] 6.20000000 2.80000000 4.80000000 1.80000000
      [128,] 6.10000000 3.00000000 4.90000000 1.80000000
      [129,] 6.40000000 2.80000000 5.60000000 2.10000000
      [130,] 7.20000000 3.00000000 5.80000000 1.60000000
      [131,] 7.40000000 2.80000000 6.10000000 1.90000000
      [132,] 7.90000000 3.80000000 6.40000000 2.00000000
      [133,] 6.40000000 2.80000000 5.60000000 2.20000000
      [134,] 6.30000000 2.80000000 5.10000000 1.50000000
      [135,] 6.10000000 2.60000000 5.60000000 1.40000000
      [136,] 7.70000000 3.00000000 6.10000000 2.30000000
      [137,] 6.30000000 3.40000000 5.60000000 2.40000000
      [138,] 6.40000000 3.10000000 5.50000000 1.80000000
      [139,] 6.00000000 3.00000000 4.80000000 1.80000000
      [140,] 6.90000000 3.10000000 5.40000000 2.10000000
      [141,] 6.70000000 3.10000000 5.60000000 2.40000000
      [142,] 6.90000000 3.10000000 5.10000000 2.30000000
      [143,] 5.80000000 2.70000000 5.10000000 1.90000000
      [144,] 6.80000000 3.20000000 5.90000000 2.30000000
      [145,] 6.70000000 3.30000000 5.70000000 2.50000000
      [146,] 6.70000000 3.00000000 5.20000000 2.30000000
      [147,] 6.30000000 2.50000000 5.00000000 1.90000000
      [148,] 6.50000000 3.00000000 5.20000000 2.00000000
      [149,] 6.20000000 3.40000000 5.40000000 2.30000000
      [150,] 5.90000000 3.00000000 5.10000000 1.80000000
      * missing =
           row col
      [1,] 22 2
      [2,] 1 3
      [3,] 12 3
      [4,] 59 3
      [5,] 11 4
      * nbSample = 150
      * nbCluster = 3
      * lnLikelihood = -1037.496
      * nbFreeParameter= 70
      * criterion name = ICL
      * criterion value= 2433.029
      * zi =
        [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
       [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
       [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
      [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
      [149] 2 2
      ****************************************
      *** Cluster: 1
      * Proportion = 0.3333333
      * Means = 5.0060000 3.4106248 1.4495169 0.2406782
      * S.D. = 0.5096155 0.3389104 0.4267190 0.2041318
      ****************************************
      *** Cluster: 2
      * Proportion = 0.3333333
      * Means = 5.936000 2.770000 4.258257 1.326000
      * S.D. = 0.5096155 0.3389104 0.4267190 0.2041318
      ****************************************
      *** Cluster: 3
      * Proportion = 0.3333333
      * Means = 6.588 2.974 5.552 2.026
      * S.D. = 0.5096155 0.3389104 0.4267190 0.2041318
      ****************************************
      > model <- learnDiagGaussian( data=x, labels= z,
      + , models = clusterDiagGaussianNames(prop = "equal")
      + , algo = "impute", nbIter = 2, epsilon = 1e-08)
      > missingValues(model)
           row col value
      > print(model)
      ****************************************
      * model name = gaussian_p_sjk
      * data =
             Sepal.Length Sepal.Width Petal.Length Petal.Width
        [1,] 5.10000000 3.50000000 1.29522460 0.20000000
        [2,] 4.90000000 3.00000000 1.40000000 0.20000000
        [3,] 4.70000000 3.20000000 1.30000000 0.20000000
        [4,] 4.60000000 3.10000000 1.50000000 0.20000000
        [5,] 5.00000000 3.60000000 1.40000000 0.20000000
        [6,] 5.40000000 3.90000000 1.70000000 0.40000000
        [7,] 4.60000000 3.40000000 1.40000000 0.30000000
        [8,] 5.00000000 3.40000000 1.50000000 0.20000000
        [9,] 4.40000000 2.90000000 1.40000000 0.20000000
       [10,] 4.90000000 3.10000000 1.50000000 0.10000000
       [11,] 5.40000000 3.70000000 1.50000000 -0.06608789
       [12,] 4.80000000 3.40000000 1.08062155 0.20000000
       [13,] 4.80000000 3.00000000 1.40000000 0.10000000
       [14,] 4.30000000 3.00000000 1.10000000 0.10000000
       [15,] 5.80000000 4.00000000 1.20000000 0.20000000
       [16,] 5.70000000 4.40000000 1.50000000 0.40000000
       [17,] 5.40000000 3.90000000 1.30000000 0.40000000
       [18,] 5.10000000 3.50000000 1.40000000 0.30000000
       [19,] 5.70000000 3.80000000 1.70000000 0.30000000
       [20,] 5.10000000 3.80000000 1.50000000 0.30000000
       [21,] 5.40000000 3.40000000 1.70000000 0.20000000
       [22,] 5.10000000 2.83124151 1.50000000 0.40000000
       [23,] 4.60000000 3.60000000 1.00000000 0.20000000
       [24,] 5.10000000 3.30000000 1.70000000 0.50000000
       [25,] 4.80000000 3.40000000 1.90000000 0.20000000
       [26,] 5.00000000 3.00000000 1.60000000 0.20000000
       [27,] 5.00000000 3.40000000 1.60000000 0.40000000
       [28,] 5.20000000 3.50000000 1.50000000 0.20000000
       [29,] 5.20000000 3.40000000 1.40000000 0.20000000
       [30,] 4.70000000 3.20000000 1.60000000 0.20000000
       [31,] 4.80000000 3.10000000 1.60000000 0.20000000
       [32,] 5.40000000 3.40000000 1.50000000 0.40000000
       [33,] 5.20000000 4.10000000 1.50000000 0.10000000
       [34,] 5.50000000 4.20000000 1.40000000 0.20000000
       [35,] 4.90000000 3.10000000 1.50000000 0.20000000
       [36,] 5.00000000 3.20000000 1.20000000 0.20000000
       [37,] 5.50000000 3.50000000 1.30000000 0.20000000
       [38,] 4.90000000 3.60000000 1.40000000 0.10000000
       [39,] 4.40000000 3.00000000 1.30000000 0.20000000
       [40,] 5.10000000 3.40000000 1.50000000 0.20000000
       [41,] 5.00000000 3.50000000 1.30000000 0.30000000
       [42,] 4.50000000 2.30000000 1.30000000 0.30000000
       [43,] 4.40000000 3.20000000 1.30000000 0.20000000
       [44,] 5.00000000 3.50000000 1.60000000 0.60000000
       [45,] 5.10000000 3.80000000 1.90000000 0.40000000
       [46,] 4.80000000 3.00000000 1.40000000 0.30000000
       [47,] 5.10000000 3.80000000 1.60000000 0.20000000
       [48,] 4.60000000 3.20000000 1.40000000 0.20000000
       [49,] 5.30000000 3.70000000 1.50000000 0.20000000
       [50,] 5.00000000 3.30000000 1.40000000 0.20000000
       [51,] 7.00000000 3.20000000 4.70000000 1.40000000
       [52,] 6.40000000 3.20000000 4.50000000 1.50000000
       [53,] 6.90000000 3.10000000 4.90000000 1.50000000
       [54,] 5.50000000 2.30000000 4.00000000 1.30000000
       [55,] 6.50000000 2.80000000 4.60000000 1.50000000
       [56,] 5.70000000 2.80000000 4.50000000 1.30000000
       [57,] 6.30000000 3.30000000 4.70000000 1.60000000
       [58,] 4.90000000 2.40000000 3.30000000 1.00000000
       [59,] 6.60000000 2.90000000 4.51286262 1.30000000
       [60,] 5.20000000 2.70000000 3.90000000 1.40000000
       [61,] 5.00000000 2.00000000 3.50000000 1.00000000
       [62,] 5.90000000 3.00000000 4.20000000 1.50000000
       [63,] 6.00000000 2.20000000 4.00000000 1.00000000
       [64,] 6.10000000 2.90000000 4.70000000 1.40000000
       [65,] 5.60000000 2.90000000 3.60000000 1.30000000
       [66,] 6.70000000 3.10000000 4.40000000 1.40000000
       [67,] 5.60000000 3.00000000 4.50000000 1.50000000
       [68,] 5.80000000 2.70000000 4.10000000 1.00000000
       [69,] 6.20000000 2.20000000 4.50000000 1.50000000
       [70,] 5.60000000 2.50000000 3.90000000 1.10000000
       [71,] 5.90000000 3.20000000 4.80000000 1.80000000
       [72,] 6.10000000 2.80000000 4.00000000 1.30000000
       [73,] 6.30000000 2.50000000 4.90000000 1.50000000
       [74,] 6.10000000 2.80000000 4.70000000 1.20000000
       [75,] 6.40000000 2.90000000 4.30000000 1.30000000
       [76,] 6.60000000 3.00000000 4.40000000 1.40000000
       [77,] 6.80000000 2.80000000 4.80000000 1.40000000
       [78,] 6.70000000 3.00000000 5.00000000 1.70000000
       [79,] 6.00000000 2.90000000 4.50000000 1.50000000
       [80,] 5.70000000 2.60000000 3.50000000 1.00000000
       [81,] 5.50000000 2.40000000 3.80000000 1.10000000
       [82,] 5.50000000 2.40000000 3.70000000 1.00000000
       [83,] 5.80000000 2.70000000 3.90000000 1.20000000
       [84,] 6.00000000 2.70000000 5.10000000 1.60000000
       [85,] 5.40000000 3.00000000 4.50000000 1.50000000
       [86,] 6.00000000 3.40000000 4.50000000 1.60000000
       [87,] 6.70000000 3.10000000 4.70000000 1.50000000
       [88,] 6.30000000 2.30000000 4.40000000 1.30000000
       [89,] 5.60000000 3.00000000 4.10000000 1.30000000
       [90,] 5.50000000 2.50000000 4.00000000 1.30000000
       [91,] 5.50000000 2.60000000 4.40000000 1.20000000
       [92,] 6.10000000 3.00000000 4.60000000 1.40000000
       [93,] 5.80000000 2.60000000 4.00000000 1.20000000
       [94,] 5.00000000 2.30000000 3.30000000 1.00000000
       [95,] 5.60000000 2.70000000 4.20000000 1.30000000
       [96,] 5.70000000 3.00000000 4.20000000 1.20000000
       [97,] 5.70000000 2.90000000 4.20000000 1.30000000
       [98,] 6.20000000 2.90000000 4.30000000 1.30000000
       [99,] 5.10000000 2.50000000 3.00000000 1.10000000
      [100,] 5.70000000 2.80000000 4.10000000 1.30000000
      [101,] 6.30000000 3.30000000 6.00000000 2.50000000
      [102,] 5.80000000 2.70000000 5.10000000 1.90000000
      [103,] 7.10000000 3.00000000 5.90000000 2.10000000
      [104,] 6.30000000 2.90000000 5.60000000 1.80000000
      [105,] 6.50000000 3.00000000 5.80000000 2.20000000
      [106,] 7.60000000 3.00000000 6.60000000 2.10000000
      [107,] 4.90000000 2.50000000 4.50000000 1.70000000
      [108,] 7.30000000 2.90000000 6.30000000 1.80000000
      [109,] 6.70000000 2.50000000 5.80000000 1.80000000
      [110,] 7.20000000 3.60000000 6.10000000 2.50000000
      [111,] 6.50000000 3.20000000 5.10000000 2.00000000
      [112,] 6.40000000 2.70000000 5.30000000 1.90000000
      [113,] 6.80000000 3.00000000 5.50000000 2.10000000
      [114,] 5.70000000 2.50000000 5.00000000 2.00000000
      [115,] 5.80000000 2.80000000 5.10000000 2.40000000
      [116,] 6.40000000 3.20000000 5.30000000 2.30000000
      [117,] 6.50000000 3.00000000 5.50000000 1.80000000
      [118,] 7.70000000 3.80000000 6.70000000 2.20000000
      [119,] 7.70000000 2.60000000 6.90000000 2.30000000
      [120,] 6.00000000 2.20000000 5.00000000 1.50000000
      [121,] 6.90000000 3.20000000 5.70000000 2.30000000
      [122,] 5.60000000 2.80000000 4.90000000 2.00000000
      [123,] 7.70000000 2.80000000 6.70000000 2.00000000
      [124,] 6.30000000 2.70000000 4.90000000 1.80000000
      [125,] 6.70000000 3.30000000 5.70000000 2.10000000
      [126,] 7.20000000 3.20000000 6.00000000 1.80000000
      [127,] 6.20000000 2.80000000 4.80000000 1.80000000
      [128,] 6.10000000 3.00000000 4.90000000 1.80000000
      [129,] 6.40000000 2.80000000 5.60000000 2.10000000
      [130,] 7.20000000 3.00000000 5.80000000 1.60000000
      [131,] 7.40000000 2.80000000 6.10000000 1.90000000
      [132,] 7.90000000 3.80000000 6.40000000 2.00000000
      [133,] 6.40000000 2.80000000 5.60000000 2.20000000
      [134,] 6.30000000 2.80000000 5.10000000 1.50000000
      [135,] 6.10000000 2.60000000 5.60000000 1.40000000
      [136,] 7.70000000 3.00000000 6.10000000 2.30000000
      [137,] 6.30000000 3.40000000 5.60000000 2.40000000
      [138,] 6.40000000 3.10000000 5.50000000 1.80000000
      [139,] 6.00000000 3.00000000 4.80000000 1.80000000
      [140,] 6.90000000 3.10000000 5.40000000 2.10000000
      [141,] 6.70000000 3.10000000 5.60000000 2.40000000
      [142,] 6.90000000 3.10000000 5.10000000 2.30000000
      [143,] 5.80000000 2.70000000 5.10000000 1.90000000
      [144,] 6.80000000 3.20000000 5.90000000 2.30000000
      [145,] 6.70000000 3.30000000 5.70000000 2.50000000
      [146,] 6.70000000 3.00000000 5.20000000 2.30000000
      [147,] 6.30000000 2.50000000 5.00000000 1.90000000
      [148,] 6.50000000 3.00000000 5.20000000 2.00000000
      [149,] 6.20000000 3.40000000 5.40000000 2.30000000
      [150,] 5.90000000 3.00000000 5.10000000 1.80000000
      * missing =
           row col
      * nbSample = 150
      * nbCluster = 3
      * lnLikelihood = -1038.367
      * nbFreeParameter= 70
      * criterion name = ICL
      * criterion value= 2434.776
      * zi =
        [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
       [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
       [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
      [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
      [149] 2 2
      ****************************************
      *** Cluster: 1
      * Proportion = 0.3333333
      * Means = 5.0060000 3.4106248 1.4495169 0.2406782
      * S.D. = 0.3489470 0.3823046 0.1800213 0.1129661
      ****************************************
      *** Cluster: 2
      * Proportion = 0.3333333
      * Means = 5.936000 2.770000 4.258257 1.326000
      * S.D. = 0.5109834 0.3106445 0.4640730 0.1957652
      ****************************************
      *** Cluster: 3
      * Proportion = 0.3333333
      * Means = 6.588 2.974 5.552 2.026
      * S.D. = 0.6294887 0.3192554 0.5463479 0.2718897
      ****************************************
      > model <- learnGamma( data=x, labels= z,
      + , models = clusterGammaNames(prop = "equal")
      + , algo = "simul", nbIter = 2, epsilon = 1e-08
      + )
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes in 'inst/doc' ... OK
• checking re-building of vignette outputs ... [35s] OK
• checking PDF version of manual ... OK
• DONE

Status: 1 ERROR, 1 NOTE