R Under development (unstable) (2023-12-12 r85669 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Rmixmod)
Loading required package: Rcpp
Rmixmod v. 2.1.10 / URI: www.mixmod.org
> data(iris)
> data <- iris[, 1:4]
> 
> mixmodCluster(data, nbCluster = c(2, 3, 4))
****************************************
*** INPUT:
****************************************
* nbCluster =  2 3 4 
* criterion =  BIC 
****************************************
*** MIXMOD Models:
* list =  Gaussian_pk_Lk_C 
* This list includes only models with free proportions.
****************************************
* data (limited to a 10x10 matrix) =
      Sepal.Length Sepal.Width Petal.Length Petal.Width
 [1,] 5.1          3.5         1.4          0.2        
 [2,] 4.9          3           1.4          0.2        
 [3,] 4.7          3.2         1.3          0.2        
 [4,] 4.6          3.1         1.5          0.2        
 [5,] 5            3.6         1.4          0.2        
 [6,] 5.4          3.9         1.7          0.4        
 [7,] 4.6          3.4         1.4          0.3        
 [8,] 5            3.4         1.5          0.2        
 [9,] 4.4          2.9         1.4          0.2        
[10,] 4.9          3.1         1.5          0.1        
* ... ...
****************************************
*** MIXMOD Strategy:
* algorithm            =  EM 
* number of tries      =  1 
* number of iterations =  200 
* epsilon              =  0.001 
*** Initialization strategy:
* algorithm            =  smallEM 
* number of tries      =  10 
* number of iterations =  5 
* epsilon              =  0.001 
* seed                 =  NULL 
****************************************


****************************************
*** BEST MODEL OUTPUT:
*** According to the BIC criterion
****************************************
* nbCluster   =  4 
* model name  =  Gaussian_pk_Lk_C 
* criterion   =  BIC(602.9183)
* likelihood  =  -221.2890 
****************************************
*** Cluster 1 
* proportion =  0.3095 
* means      =  5.9258 2.7665 4.2189 1.3036 
* variances  = |     0.2043     0.0698     0.1260     0.0337 |
               |     0.0698     0.0909     0.0491     0.0265 |
               |     0.1260     0.0491     0.1412     0.0354 |
               |     0.0337     0.0265     0.0354     0.0320 |
*** Cluster 2 
* proportion =  0.1317 
* means      =  5.2666 3.7572 1.4466 0.2519 
* variances  = |     0.1224     0.0418     0.0755     0.0202 |
               |     0.0418     0.0544     0.0294     0.0159 |
               |     0.0755     0.0294     0.0846     0.0212 |
               |     0.0202     0.0159     0.0212     0.0192 |
*** Cluster 3 
* proportion =  0.2016 
* means      =  4.8358 3.2129 1.4721 0.2422 
* variances  = |     0.1087     0.0371     0.0670     0.0179 |
               |     0.0371     0.0484     0.0261     0.0141 |
               |     0.0670     0.0261     0.0751     0.0188 |
               |     0.0179     0.0141     0.0188     0.0170 |
*** Cluster 4 
* proportion =  0.3571 
* means      =  6.5534 2.9634 5.5016 1.9988 
* variances  = |     0.3864     0.1320     0.2382     0.0637 |
               |     0.1320     0.1718     0.0928     0.0501 |
               |     0.2382     0.0928     0.2669     0.0669 |
               |     0.0637     0.0501     0.0669     0.0605 |
****************************************
> 
> proc.time()
   user  system elapsed 
   0.32    0.06    0.37