Gaussian mixture models for compositional data using the alphatransformation {Compositional}  R Documentation 
Gaussian mixture models for compositional data using the αtransformation.
alfa.mix.norm(x, g, a, model, veo = FALSE)
x 
A matrix with the compositional data. 
g 
How many clusters to create. 
a 
The value of the power transformation, it has to be between 1 and 1. If zero values are present it has to be greater than 0. If α=0 the isometric logratio transformation is applied. 
model 
The type of model to be used.

veo 
Stands for "Variables exceed observations". If TRUE then if the number variablesin the model exceeds the number of observations, but the model is still fitted. 
A logratio transformation is applied and then a Gaussian mixture model is constructed.
A list including:
mu 
A matrix where each row corresponds to the mean vector of each cluster. 
su 
An array containing the covariance matrix of each cluster. 
prob 
The estimated mixing probabilities. 
est 
The estimated cluster membership values. 
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas (2015). R package mixture: Mixture Models for Clustering and Classification.
Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A databased power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
bic.alfamixnorm, bic.mixcompnorm, rmixcomp, mixnorm.contour, mix.compnorm,
alfa, alfa.knn, alfa.rda, comp.nb
## Not run: x < as.matrix(iris[, 1:4]) x < x/ rowSums(x) mod1 < alfa.mix.norm(x, 3, 0.4, model = "EII" ) mod2 < alfa.mix.norm(x, 4, 0.7, model = "VII") ## End(Not run)