# R/ce.4betaNB.AIC.R In breakpoint: An R Package for Multiple Break-Point Detection via the Cross-Entropy Method

#### Defines functions ce.4betaNB.AIC

```ce.4betaNB.AIC <-
function(N, data, h, L0, L, M, Melite, eps, a, r){

if (N == 0){
loglik.full <- logliknb(1, (L + 1), data, r, h)[[1]]
AIC.full <- AICnb(loglik.full, 0)
return(list(loci = c(1, (L + 1)), AIC = AIC.full, LogLike = loglik.full))

} else {

######################### Parameter initialization #####################
new.para <- array(1, dim = c(2, N))
########################################################################
#  aic <- c()
k <- 0

repeat
{
k <- k + 1
ch <- array(0, dim = c(M, (N + 2)))
ch[, 1] <- c(1)
ch[, (N + 2)] <- c(L + 1)
ch[, (2 : (N + 1))] <- apply(new.para, 2, betarand, L0, L, M)
ch <- t(apply(ch, 1, sort))
loglike <- apply(ch, 1, llhoodnb, data, r, h)
aic.vals <- apply(as.data.frame(loglike), 1, AICnb, N)
ch <- cbind(ch, aic.vals, loglike)
ch <- ch[order(ch[, (N + 3)], decreasing = FALSE), ]
melitesmpl <- ch[1:Melite, ]
#    aic[k] <- melitesmpl[1, (N + 3)]

newpar.n <- array(0, dim = c(2, N))
newpar.n[1, ] <- apply(as.matrix(melitesmpl[, (2 : (N + 1))]), 2, mean)
newpar.n[2, ] <- apply(as.matrix(melitesmpl[, (2 : (N + 1))]), 2, var)

newpar.new <- array(0, dim = c(2, N))
newpar.new[1, ] <- apply(newpar.n, 2, fun.alpha, L0, L)
newpar.new[2, ] <- apply(newpar.n, 2, fun.beta, L0, L)
new.para <- a * newpar.new + (1 - a) * new.para