#' Selection criteria based on maximized log-likelihood function value
#'
#' \code{measures_loglik} calculates the AiC, AICc, BIC, HQIC, CAIC measures.
#' @param n Number of systems.
#' @param npar Number of parameters.
#' @param loglik Maximized log-likelihood function value returned in \code{\link{EMalg}}.
#'
#' @return A list with the following components:
#' \item{loglik}{Maximized log-likelihood function value.}
#' \item{aic}{AIC (Akaike Information Criterion).}
#' \item{aicc}{AICc (Corrected Akaike Information Criterion).}
#' \item{bic}{BIC (Bayesian Information Criterion).}
#' \item{hqic}{HQIC (Hannan-Quinn Information Criterion).}
#' \item{caic}{CAIC (Consistent Akaike Information Criterion).}
#' @export
#'
measures_loglik <- function(n,npar,loglik){
aic <- -2*loglik + 2*npar
aicc <- aic + (2*npar*(npar+1))/(n-npar-1)
bic <- -2*loglik + npar*log(n)
hqic <- -2*loglik + 2*npar*log(log(n))
caic <- -2*loglik + npar*(log(n)+1)
return(list(loglik=loglik,aic=aic,aicc=aicc,bic=bic,hqic=hqic,caic=caic))
}
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