CRAN Package Check Results for Package MixAll

Last updated on 2021-09-25 20:50:33 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.5.1 728.36 107.53 835.89 OK
r-devel-linux-x86_64-debian-gcc 1.5.1 490.46 82.67 573.13 ERROR
r-devel-linux-x86_64-fedora-clang 1.5.1 1229.69 NOTE
r-devel-linux-x86_64-fedora-gcc 1.5.1 1120.79 OK
r-devel-windows-x86_64 1.5.1 670.00 128.00 798.00 OK
r-devel-windows-x86_64-gcc10-UCRT 1.5.1 OK
r-patched-linux-x86_64 1.5.1 593.84 103.45 697.29 OK
r-patched-solaris-x86 1.5.1 929.50 OK
r-release-linux-x86_64 1.5.1 590.31 103.76 694.07 OK
r-release-macos-arm64 1.5.1 NOTE
r-release-macos-x86_64 1.5.1 NOTE
r-release-windows-ix86+x86_64 1.5.1 1670.00 217.00 1887.00 NOTE
r-oldrel-macos-x86_64 1.5.1 NOTE
r-oldrel-windows-ix86+x86_64 1.5.1 1235.00 177.00 1412.00 NOTE

Check Details

Version: 1.5.1
Check: tests
Result: ERROR
     Running ‘ClusterSimul.R’ [0s/1s]
     Running ‘clusterDiagGaussianLikelihood.R’ [1s/2s]
     Running ‘clusterGammaLikelihood.R’ [1s/1s]
     Running ‘simulHeterogeneous.R’ [0s/1s]
     Running ‘simulNonLinear.R’ [0s/1s]
     Running ‘testAllLearners.R’ [1s/2s]
     Running ‘testPoissonExample.R’ [1s/2s]
     Running ‘testPredict.R’ [7s/11s]
    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,] 148 3 5.2
     [2,] 7 4 0.3
     [3,] 45 3 1.9
     [4,] 111 3 5.1
     [5,] 27 3 1.6
     > 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 27 3 1.6171218
     2 45 3 1.4404766
     3 111 3 5.6841796
     4 148 3 5.2681986
     5 7 4 -0.2719283
     > print(model)
     ****************************************
     * model name = gaussian_p_s
     * data =
     Sepal.Length Sepal.Width Petal.Length Petal.Width
     [1,] 5.1000000 3.5000000 1.4000000 0.2000000
     [2,] 4.9000000 3.0000000 1.4000000 0.2000000
     [3,] 4.7000000 3.2000000 1.3000000 0.2000000
     [4,] 4.6000000 3.1000000 1.5000000 0.2000000
     [5,] 5.0000000 3.6000000 1.4000000 0.2000000
     [6,] 5.4000000 3.9000000 1.7000000 0.4000000
     [7,] 4.6000000 3.4000000 1.4000000 -0.2719283
     [8,] 5.0000000 3.4000000 1.5000000 0.2000000
     [9,] 4.4000000 2.9000000 1.4000000 0.2000000
     [10,] 4.9000000 3.1000000 1.5000000 0.1000000
     [11,] 5.4000000 3.7000000 1.5000000 0.2000000
     [12,] 4.8000000 3.4000000 1.6000000 0.2000000
     [13,] 4.8000000 3.0000000 1.4000000 0.1000000
     [14,] 4.3000000 3.0000000 1.1000000 0.1000000
     [15,] 5.8000000 4.0000000 1.2000000 0.2000000
     [16,] 5.7000000 4.4000000 1.5000000 0.4000000
     [17,] 5.4000000 3.9000000 1.3000000 0.4000000
     [18,] 5.1000000 3.5000000 1.4000000 0.3000000
     [19,] 5.7000000 3.8000000 1.7000000 0.3000000
     [20,] 5.1000000 3.8000000 1.5000000 0.3000000
     [21,] 5.4000000 3.4000000 1.7000000 0.2000000
     [22,] 5.1000000 3.7000000 1.5000000 0.4000000
     [23,] 4.6000000 3.6000000 1.0000000 0.2000000
     [24,] 5.1000000 3.3000000 1.7000000 0.5000000
     [25,] 4.8000000 3.4000000 1.9000000 0.2000000
     [26,] 5.0000000 3.0000000 1.6000000 0.2000000
     [27,] 5.0000000 3.4000000 1.6171218 0.4000000
     [28,] 5.2000000 3.5000000 1.5000000 0.2000000
     [29,] 5.2000000 3.4000000 1.4000000 0.2000000
     [30,] 4.7000000 3.2000000 1.6000000 0.2000000
     [31,] 4.8000000 3.1000000 1.6000000 0.2000000
     [32,] 5.4000000 3.4000000 1.5000000 0.4000000
     [33,] 5.2000000 4.1000000 1.5000000 0.1000000
     [34,] 5.5000000 4.2000000 1.4000000 0.2000000
     [35,] 4.9000000 3.1000000 1.5000000 0.2000000
     [36,] 5.0000000 3.2000000 1.2000000 0.2000000
     [37,] 5.5000000 3.5000000 1.3000000 0.2000000
     [38,] 4.9000000 3.6000000 1.4000000 0.1000000
     [39,] 4.4000000 3.0000000 1.3000000 0.2000000
     [40,] 5.1000000 3.4000000 1.5000000 0.2000000
     [41,] 5.0000000 3.5000000 1.3000000 0.3000000
     [42,] 4.5000000 2.3000000 1.3000000 0.3000000
     [43,] 4.4000000 3.2000000 1.3000000 0.2000000
     [44,] 5.0000000 3.5000000 1.6000000 0.6000000
     [45,] 5.1000000 3.8000000 1.4404766 0.4000000
     [46,] 4.8000000 3.0000000 1.4000000 0.3000000
     [47,] 5.1000000 3.8000000 1.6000000 0.2000000
     [48,] 4.6000000 3.2000000 1.4000000 0.2000000
     [49,] 5.3000000 3.7000000 1.5000000 0.2000000
     [50,] 5.0000000 3.3000000 1.4000000 0.2000000
     [51,] 7.0000000 3.2000000 4.7000000 1.4000000
     [52,] 6.4000000 3.2000000 4.5000000 1.5000000
     [53,] 6.9000000 3.1000000 4.9000000 1.5000000
     [54,] 5.5000000 2.3000000 4.0000000 1.3000000
     [55,] 6.5000000 2.8000000 4.6000000 1.5000000
     [56,] 5.7000000 2.8000000 4.5000000 1.3000000
     [57,] 6.3000000 3.3000000 4.7000000 1.6000000
     [58,] 4.9000000 2.4000000 3.3000000 1.0000000
     [59,] 6.6000000 2.9000000 4.6000000 1.3000000
     [60,] 5.2000000 2.7000000 3.9000000 1.4000000
     [61,] 5.0000000 2.0000000 3.5000000 1.0000000
     [62,] 5.9000000 3.0000000 4.2000000 1.5000000
     [63,] 6.0000000 2.2000000 4.0000000 1.0000000
     [64,] 6.1000000 2.9000000 4.7000000 1.4000000
     [65,] 5.6000000 2.9000000 3.6000000 1.3000000
     [66,] 6.7000000 3.1000000 4.4000000 1.4000000
     [67,] 5.6000000 3.0000000 4.5000000 1.5000000
     [68,] 5.8000000 2.7000000 4.1000000 1.0000000
     [69,] 6.2000000 2.2000000 4.5000000 1.5000000
     [70,] 5.6000000 2.5000000 3.9000000 1.1000000
     [71,] 5.9000000 3.2000000 4.8000000 1.8000000
     [72,] 6.1000000 2.8000000 4.0000000 1.3000000
     [73,] 6.3000000 2.5000000 4.9000000 1.5000000
     [74,] 6.1000000 2.8000000 4.7000000 1.2000000
     [75,] 6.4000000 2.9000000 4.3000000 1.3000000
     [76,] 6.6000000 3.0000000 4.4000000 1.4000000
     [77,] 6.8000000 2.8000000 4.8000000 1.4000000
     [78,] 6.7000000 3.0000000 5.0000000 1.7000000
     [79,] 6.0000000 2.9000000 4.5000000 1.5000000
     [80,] 5.7000000 2.6000000 3.5000000 1.0000000
     [81,] 5.5000000 2.4000000 3.8000000 1.1000000
     [82,] 5.5000000 2.4000000 3.7000000 1.0000000
     [83,] 5.8000000 2.7000000 3.9000000 1.2000000
     [84,] 6.0000000 2.7000000 5.1000000 1.6000000
     [85,] 5.4000000 3.0000000 4.5000000 1.5000000
     [86,] 6.0000000 3.4000000 4.5000000 1.6000000
     [87,] 6.7000000 3.1000000 4.7000000 1.5000000
     [88,] 6.3000000 2.3000000 4.4000000 1.3000000
     [89,] 5.6000000 3.0000000 4.1000000 1.3000000
     [90,] 5.5000000 2.5000000 4.0000000 1.3000000
     [91,] 5.5000000 2.6000000 4.4000000 1.2000000
     [92,] 6.1000000 3.0000000 4.6000000 1.4000000
     [93,] 5.8000000 2.6000000 4.0000000 1.2000000
     [94,] 5.0000000 2.3000000 3.3000000 1.0000000
     [95,] 5.6000000 2.7000000 4.2000000 1.3000000
     [96,] 5.7000000 3.0000000 4.2000000 1.2000000
     [97,] 5.7000000 2.9000000 4.2000000 1.3000000
     [98,] 6.2000000 2.9000000 4.3000000 1.3000000
     [99,] 5.1000000 2.5000000 3.0000000 1.1000000
     [100,] 5.7000000 2.8000000 4.1000000 1.3000000
     [101,] 6.3000000 3.3000000 6.0000000 2.5000000
     [102,] 5.8000000 2.7000000 5.1000000 1.9000000
     [103,] 7.1000000 3.0000000 5.9000000 2.1000000
     [104,] 6.3000000 2.9000000 5.6000000 1.8000000
     [105,] 6.5000000 3.0000000 5.8000000 2.2000000
     [106,] 7.6000000 3.0000000 6.6000000 2.1000000
     [107,] 4.9000000 2.5000000 4.5000000 1.7000000
     [108,] 7.3000000 2.9000000 6.3000000 1.8000000
     [109,] 6.7000000 2.5000000 5.8000000 1.8000000
     [110,] 7.2000000 3.6000000 6.1000000 2.5000000
     [111,] 6.5000000 3.2000000 5.6841796 2.0000000
     [112,] 6.4000000 2.7000000 5.3000000 1.9000000
     [113,] 6.8000000 3.0000000 5.5000000 2.1000000
     [114,] 5.7000000 2.5000000 5.0000000 2.0000000
     [115,] 5.8000000 2.8000000 5.1000000 2.4000000
     [116,] 6.4000000 3.2000000 5.3000000 2.3000000
     [117,] 6.5000000 3.0000000 5.5000000 1.8000000
     [118,] 7.7000000 3.8000000 6.7000000 2.2000000
     [119,] 7.7000000 2.6000000 6.9000000 2.3000000
     [120,] 6.0000000 2.2000000 5.0000000 1.5000000
     [121,] 6.9000000 3.2000000 5.7000000 2.3000000
     [122,] 5.6000000 2.8000000 4.9000000 2.0000000
     [123,] 7.7000000 2.8000000 6.7000000 2.0000000
     [124,] 6.3000000 2.7000000 4.9000000 1.8000000
     [125,] 6.7000000 3.3000000 5.7000000 2.1000000
     [126,] 7.2000000 3.2000000 6.0000000 1.8000000
     [127,] 6.2000000 2.8000000 4.8000000 1.8000000
     [128,] 6.1000000 3.0000000 4.9000000 1.8000000
     [129,] 6.4000000 2.8000000 5.6000000 2.1000000
     [130,] 7.2000000 3.0000000 5.8000000 1.6000000
     [131,] 7.4000000 2.8000000 6.1000000 1.9000000
     [132,] 7.9000000 3.8000000 6.4000000 2.0000000
     [133,] 6.4000000 2.8000000 5.6000000 2.2000000
     [134,] 6.3000000 2.8000000 5.1000000 1.5000000
     [135,] 6.1000000 2.6000000 5.6000000 1.4000000
     [136,] 7.7000000 3.0000000 6.1000000 2.3000000
     [137,] 6.3000000 3.4000000 5.6000000 2.4000000
     [138,] 6.4000000 3.1000000 5.5000000 1.8000000
     [139,] 6.0000000 3.0000000 4.8000000 1.8000000
     [140,] 6.9000000 3.1000000 5.4000000 2.1000000
     [141,] 6.7000000 3.1000000 5.6000000 2.4000000
     [142,] 6.9000000 3.1000000 5.1000000 2.3000000
     [143,] 5.8000000 2.7000000 5.1000000 1.9000000
     [144,] 6.8000000 3.2000000 5.9000000 2.3000000
     [145,] 6.7000000 3.3000000 5.7000000 2.5000000
     [146,] 6.7000000 3.0000000 5.2000000 2.3000000
     [147,] 6.3000000 2.5000000 5.0000000 1.9000000
     [148,] 6.5000000 3.0000000 5.2681986 2.0000000
     [149,] 6.2000000 3.4000000 5.4000000 2.3000000
     [150,] 5.9000000 3.0000000 5.1000000 1.8000000
     * missing =
     row col
     [1,] 27 3
     [2,] 45 3
     [3,] 111 3
     [4,] 148 3
     [5,] 7 4
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1032.27
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2422.437
     * 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 3.4280000 1.4531520 0.2345614
     * S.D. = 0.3854158 0.3854158 0.3854158 0.3854158
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936 2.770 4.260 1.326
     * S.D. = 0.3854158 0.3854158 0.3854158 0.3854158
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974000 5.565048 2.026000
     * S.D. = 0.3854158 0.3854158 0.3854158 0.3854158
     ****************************************
     > 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,] 5.1000000 3.5000000 1.4000000 0.2000000
     [2,] 4.9000000 3.0000000 1.4000000 0.2000000
     [3,] 4.7000000 3.2000000 1.3000000 0.2000000
     [4,] 4.6000000 3.1000000 1.5000000 0.2000000
     [5,] 5.0000000 3.6000000 1.4000000 0.2000000
     [6,] 5.4000000 3.9000000 1.7000000 0.4000000
     [7,] 4.6000000 3.4000000 1.4000000 -0.2719283
     [8,] 5.0000000 3.4000000 1.5000000 0.2000000
     [9,] 4.4000000 2.9000000 1.4000000 0.2000000
     [10,] 4.9000000 3.1000000 1.5000000 0.1000000
     [11,] 5.4000000 3.7000000 1.5000000 0.2000000
     [12,] 4.8000000 3.4000000 1.6000000 0.2000000
     [13,] 4.8000000 3.0000000 1.4000000 0.1000000
     [14,] 4.3000000 3.0000000 1.1000000 0.1000000
     [15,] 5.8000000 4.0000000 1.2000000 0.2000000
     [16,] 5.7000000 4.4000000 1.5000000 0.4000000
     [17,] 5.4000000 3.9000000 1.3000000 0.4000000
     [18,] 5.1000000 3.5000000 1.4000000 0.3000000
     [19,] 5.7000000 3.8000000 1.7000000 0.3000000
     [20,] 5.1000000 3.8000000 1.5000000 0.3000000
     [21,] 5.4000000 3.4000000 1.7000000 0.2000000
     [22,] 5.1000000 3.7000000 1.5000000 0.4000000
     [23,] 4.6000000 3.6000000 1.0000000 0.2000000
     [24,] 5.1000000 3.3000000 1.7000000 0.5000000
     [25,] 4.8000000 3.4000000 1.9000000 0.2000000
     [26,] 5.0000000 3.0000000 1.6000000 0.2000000
     [27,] 5.0000000 3.4000000 1.6171218 0.4000000
     [28,] 5.2000000 3.5000000 1.5000000 0.2000000
     [29,] 5.2000000 3.4000000 1.4000000 0.2000000
     [30,] 4.7000000 3.2000000 1.6000000 0.2000000
     [31,] 4.8000000 3.1000000 1.6000000 0.2000000
     [32,] 5.4000000 3.4000000 1.5000000 0.4000000
     [33,] 5.2000000 4.1000000 1.5000000 0.1000000
     [34,] 5.5000000 4.2000000 1.4000000 0.2000000
     [35,] 4.9000000 3.1000000 1.5000000 0.2000000
     [36,] 5.0000000 3.2000000 1.2000000 0.2000000
     [37,] 5.5000000 3.5000000 1.3000000 0.2000000
     [38,] 4.9000000 3.6000000 1.4000000 0.1000000
     [39,] 4.4000000 3.0000000 1.3000000 0.2000000
     [40,] 5.1000000 3.4000000 1.5000000 0.2000000
     [41,] 5.0000000 3.5000000 1.3000000 0.3000000
     [42,] 4.5000000 2.3000000 1.3000000 0.3000000
     [43,] 4.4000000 3.2000000 1.3000000 0.2000000
     [44,] 5.0000000 3.5000000 1.6000000 0.6000000
     [45,] 5.1000000 3.8000000 1.4404766 0.4000000
     [46,] 4.8000000 3.0000000 1.4000000 0.3000000
     [47,] 5.1000000 3.8000000 1.6000000 0.2000000
     [48,] 4.6000000 3.2000000 1.4000000 0.2000000
     [49,] 5.3000000 3.7000000 1.5000000 0.2000000
     [50,] 5.0000000 3.3000000 1.4000000 0.2000000
     [51,] 7.0000000 3.2000000 4.7000000 1.4000000
     [52,] 6.4000000 3.2000000 4.5000000 1.5000000
     [53,] 6.9000000 3.1000000 4.9000000 1.5000000
     [54,] 5.5000000 2.3000000 4.0000000 1.3000000
     [55,] 6.5000000 2.8000000 4.6000000 1.5000000
     [56,] 5.7000000 2.8000000 4.5000000 1.3000000
     [57,] 6.3000000 3.3000000 4.7000000 1.6000000
     [58,] 4.9000000 2.4000000 3.3000000 1.0000000
     [59,] 6.6000000 2.9000000 4.6000000 1.3000000
     [60,] 5.2000000 2.7000000 3.9000000 1.4000000
     [61,] 5.0000000 2.0000000 3.5000000 1.0000000
     [62,] 5.9000000 3.0000000 4.2000000 1.5000000
     [63,] 6.0000000 2.2000000 4.0000000 1.0000000
     [64,] 6.1000000 2.9000000 4.7000000 1.4000000
     [65,] 5.6000000 2.9000000 3.6000000 1.3000000
     [66,] 6.7000000 3.1000000 4.4000000 1.4000000
     [67,] 5.6000000 3.0000000 4.5000000 1.5000000
     [68,] 5.8000000 2.7000000 4.1000000 1.0000000
     [69,] 6.2000000 2.2000000 4.5000000 1.5000000
     [70,] 5.6000000 2.5000000 3.9000000 1.1000000
     [71,] 5.9000000 3.2000000 4.8000000 1.8000000
     [72,] 6.1000000 2.8000000 4.0000000 1.3000000
     [73,] 6.3000000 2.5000000 4.9000000 1.5000000
     [74,] 6.1000000 2.8000000 4.7000000 1.2000000
     [75,] 6.4000000 2.9000000 4.3000000 1.3000000
     [76,] 6.6000000 3.0000000 4.4000000 1.4000000
     [77,] 6.8000000 2.8000000 4.8000000 1.4000000
     [78,] 6.7000000 3.0000000 5.0000000 1.7000000
     [79,] 6.0000000 2.9000000 4.5000000 1.5000000
     [80,] 5.7000000 2.6000000 3.5000000 1.0000000
     [81,] 5.5000000 2.4000000 3.8000000 1.1000000
     [82,] 5.5000000 2.4000000 3.7000000 1.0000000
     [83,] 5.8000000 2.7000000 3.9000000 1.2000000
     [84,] 6.0000000 2.7000000 5.1000000 1.6000000
     [85,] 5.4000000 3.0000000 4.5000000 1.5000000
     [86,] 6.0000000 3.4000000 4.5000000 1.6000000
     [87,] 6.7000000 3.1000000 4.7000000 1.5000000
     [88,] 6.3000000 2.3000000 4.4000000 1.3000000
     [89,] 5.6000000 3.0000000 4.1000000 1.3000000
     [90,] 5.5000000 2.5000000 4.0000000 1.3000000
     [91,] 5.5000000 2.6000000 4.4000000 1.2000000
     [92,] 6.1000000 3.0000000 4.6000000 1.4000000
     [93,] 5.8000000 2.6000000 4.0000000 1.2000000
     [94,] 5.0000000 2.3000000 3.3000000 1.0000000
     [95,] 5.6000000 2.7000000 4.2000000 1.3000000
     [96,] 5.7000000 3.0000000 4.2000000 1.2000000
     [97,] 5.7000000 2.9000000 4.2000000 1.3000000
     [98,] 6.2000000 2.9000000 4.3000000 1.3000000
     [99,] 5.1000000 2.5000000 3.0000000 1.1000000
     [100,] 5.7000000 2.8000000 4.1000000 1.3000000
     [101,] 6.3000000 3.3000000 6.0000000 2.5000000
     [102,] 5.8000000 2.7000000 5.1000000 1.9000000
     [103,] 7.1000000 3.0000000 5.9000000 2.1000000
     [104,] 6.3000000 2.9000000 5.6000000 1.8000000
     [105,] 6.5000000 3.0000000 5.8000000 2.2000000
     [106,] 7.6000000 3.0000000 6.6000000 2.1000000
     [107,] 4.9000000 2.5000000 4.5000000 1.7000000
     [108,] 7.3000000 2.9000000 6.3000000 1.8000000
     [109,] 6.7000000 2.5000000 5.8000000 1.8000000
     [110,] 7.2000000 3.6000000 6.1000000 2.5000000
     [111,] 6.5000000 3.2000000 5.6841796 2.0000000
     [112,] 6.4000000 2.7000000 5.3000000 1.9000000
     [113,] 6.8000000 3.0000000 5.5000000 2.1000000
     [114,] 5.7000000 2.5000000 5.0000000 2.0000000
     [115,] 5.8000000 2.8000000 5.1000000 2.4000000
     [116,] 6.4000000 3.2000000 5.3000000 2.3000000
     [117,] 6.5000000 3.0000000 5.5000000 1.8000000
     [118,] 7.7000000 3.8000000 6.7000000 2.2000000
     [119,] 7.7000000 2.6000000 6.9000000 2.3000000
     [120,] 6.0000000 2.2000000 5.0000000 1.5000000
     [121,] 6.9000000 3.2000000 5.7000000 2.3000000
     [122,] 5.6000000 2.8000000 4.9000000 2.0000000
     [123,] 7.7000000 2.8000000 6.7000000 2.0000000
     [124,] 6.3000000 2.7000000 4.9000000 1.8000000
     [125,] 6.7000000 3.3000000 5.7000000 2.1000000
     [126,] 7.2000000 3.2000000 6.0000000 1.8000000
     [127,] 6.2000000 2.8000000 4.8000000 1.8000000
     [128,] 6.1000000 3.0000000 4.9000000 1.8000000
     [129,] 6.4000000 2.8000000 5.6000000 2.1000000
     [130,] 7.2000000 3.0000000 5.8000000 1.6000000
     [131,] 7.4000000 2.8000000 6.1000000 1.9000000
     [132,] 7.9000000 3.8000000 6.4000000 2.0000000
     [133,] 6.4000000 2.8000000 5.6000000 2.2000000
     [134,] 6.3000000 2.8000000 5.1000000 1.5000000
     [135,] 6.1000000 2.6000000 5.6000000 1.4000000
     [136,] 7.7000000 3.0000000 6.1000000 2.3000000
     [137,] 6.3000000 3.4000000 5.6000000 2.4000000
     [138,] 6.4000000 3.1000000 5.5000000 1.8000000
     [139,] 6.0000000 3.0000000 4.8000000 1.8000000
     [140,] 6.9000000 3.1000000 5.4000000 2.1000000
     [141,] 6.7000000 3.1000000 5.6000000 2.4000000
     [142,] 6.9000000 3.1000000 5.1000000 2.3000000
     [143,] 5.8000000 2.7000000 5.1000000 1.9000000
     [144,] 6.8000000 3.2000000 5.9000000 2.3000000
     [145,] 6.7000000 3.3000000 5.7000000 2.5000000
     [146,] 6.7000000 3.0000000 5.2000000 2.3000000
     [147,] 6.3000000 2.5000000 5.0000000 1.9000000
     [148,] 6.5000000 3.0000000 5.2681986 2.0000000
     [149,] 6.2000000 3.4000000 5.4000000 2.3000000
     [150,] 5.9000000 3.0000000 5.1000000 1.8000000
     * missing =
     row col
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1030.729
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2419.581
     * 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 3.4280000 1.4531520 0.2345614
     * S.D. = 0.3489470 0.3752546 0.1604695 0.1267274
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936 2.770 4.260 1.326
     * S.D. = 0.5109834 0.3106445 0.4651881 0.1957652
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974000 5.565048 2.026000
     * S.D. = 0.6294887 0.3192554 0.5419612 0.2718897
     ****************************************
     > model <- learnGamma( data=x, labels= z,
     + , models = clusterGammaNames(prop = "equal")
     + , algo = "simul", nbIter = 2, epsilon = 1e-08
     + )
    
     *** caught segfault ***
     address 0x120, cause 'memory not mapped'
    
     Traceback:
     1: learnGamma(data = x, labels = z, , models = clusterGammaNames(prop = "equal"), algo = "simul", nbIter = 2, epsilon = 1e-08)
     An irrecoverable exception occurred. R is aborting now ...
     Segmentation fault
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.5.1
Check: installed package size
Result: NOTE
     installed size is 26.9Mb
     sub-directories of 1Mb or more:
     libs 24.6Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64