model { 
    for(i in 1:n) {
        # Structural model
        for(k in 1:lat) {
            eta[i , k] ~ dt(mu.eta[i , k] , psi.prec[k], nu[k])
        }   
    }
    
    for(i in 1:n) {
    
        # Regression model
        # Note. 'REGRESSIONMODEL' is replaced by the R script
                   
        #REGRESSIONMODEL
        
        # Note. 'REGRESSIONMODEL' is replaced by the R script 
        
    }
                     
    #priors on eta precisions, alpha and fixed factor loadings
    for(k in 1:lat) {
        psi[k] <- 1 / psi.prec[k]
        psi.prec[k] ~ dgamma(.001, .001)
        alpha[k] ~ dnorm(0,0.0001)
        nu[k] <- nu.prec[k] + 1
        nu.prec[k] ~ dexp(1 / 29)
    }
        
    #priors on beta coefficients 
    for (m in 1:length(b.priors[,1])) {
        beta[ b.priors[m,1] , b.priors[m,2] ] ~ dnorm(0 , 0.0001)
    }
    
    # Additional computations
    # Note. 'ADDITIONAL' is replaced by the R script
               
    #ADDITIONAL
    
    # Note. 'ADDITIONAL' is replaced by the R script 
    
}