Nothing
#Optimization for the Global Covariance Matrix
opt.sigma <- function(nu, lambda, mu, sigprior) {
#find the covariance
if(ncol(mu)==1) {
covariance <- crossprod(sweep(lambda, 2, STATS=as.numeric(mu), FUN="-"))
} else {
covariance <- crossprod(lambda-t(mu))
}
sigma <- (covariance + nu)/nrow(lambda) #add to estimation variance
sigma <- diag(diag(sigma),nrow=nrow(nu))*sigprior + (1-sigprior)*sigma #weight by the prior
return(sigma)
}
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