This function relabels the MCMC output by simply ordering a specific parameter. Let m, K and J denote the number of simulated MCMC samples, number of mixture components and different parameter types, respectively.
1  aic(mcmc.pars, constraint)

mcmc.pars 
m\times K\times J array of simulated MCMC parameters. 
constraint 
An integer between 1 and J corresponding to the parameter that will be used to apply the Identifiabiality Constraint. In this case, the MCMC output is reordered according to the constraint mcmc.pars[i,1,constraint] < … < mcmc.pars[i,K,constraint], for all i=1,…,m. If 
permutations 
an m\times K array of permutations. 
Panagiotis Papastamoulis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  #load a toy example: MCMC output consists of the random beta model
# applied to a normal mixture of \code{K=2} components. The number of
# observations is equal to \code{n=5}. The number of MCMC samples is
# equal to \code{m=300}. The 1000 generated MCMC samples are stored
#to array mcmc.pars.
data("mcmc_output")
mcmc.pars<data_list$"mcmc.pars"
# mcmc parameters are stored to array \code{mcmc.pars}
# mcmc.pars[,,1]: simulated means of the two components
# mcmc.pars[,,2]: simulated variances of the two components
# mcmc.pars[,,3]: simulated weights of the two components
# We will apply AIC by ordering the means
# which corresponds to value \code{constraint=1}
run<aic(mcmc = mcmc.pars,constraint=1)
# apply the permutations returned by typing:
reordered.mcmc<permute.mcmc(mcmc.pars,run$permutations)
# reordered.mcmc[,,1]: reordered means of the two components
# reordered.mcmc[,,2]: reordered variances of the components
# reordered.mcmc[,,3]: reordered weights

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