# likSISRAR1 = function(theta, data, ARMAorder, CountDist){
# MargParmIndices = switch(CountDist,
# "Poisson" = 1,
# "Negative Binomial" = 1:2,
# "Mixed Poisson" = 1:3,
# "Generalized Poisson" = 1:2,
# "Binomial" = 1:2)
#
# # retrieve marginal cdf
# cdf = switch(CountDist,
# "Poisson" = ppois,
# "Negative Binomial" = function(x, theta){ pnbinom(q = x, size = theta[1], prob = 1-theta[2]) },
# "Mixed Poisson" = function(x, theta){ pmixpois(x, p = theta[1], lam1 = theta[2], lam2 = theta[3])},
# "Generalized Poisson" = pGenPoisson,
# "Binomial" = pbinom
# )
#
# # retrieve marginal pdf
# pdf = switch(CountDist,
# "Poisson" = dpois,
# "Negative Binomial" = function(x, theta){ dnbinom(x, size = theta[1], prob = theta[2]) },
# "Mixed Poisson" = function(x, theta){ dmixpois(x, p = theta[1], lam1 = theta[2], lam2 = theta[3])},
# "Generalized Poisson" = dGenPoisson,
# "Binomial" = dbinom
# )
#
# #set.seed(1)
# theta1.idx = MargParmIndices
# theta2.idx = ARMAorder[1]
# theta1 = theta[theta1.idx]
# n.theta1.idx = theta1.idx[length(theta1.idx)] # num params in theta1
# theta2.idx = (n.theta1.idx + 1):(n.theta1.idx + 1)
# phi = theta[theta2.idx]
# xt = data
# T1 = length(xt)
# N = 1000 # number of particles
# prt = matrix(0,N,T1) # to collect all particles
# wgh = matrix(0,N,T1) # to collect all particle weights
#
# a = qnorm(cdf(xt[1]-1,theta1),0,1)
# b = qnorm(cdf(xt[1],theta1),0,1)
# a = rep(a,N)
# b = rep(b,N)
# zprev = z.rest(a,b)
# zhat = phi*zprev
# prt[,1] = zhat
#
# nloglik <- 0
# for (t in 2:T1)
# {
# rt = sqrt(1-phi^2)
# a = (qnorm(cdf(xt[t]-1,theta1),0,1) - phi*zprev)/rt
# b = (qnorm(cdf(xt[t],theta1),0,1) - phi*zprev)/rt
# err = z.rest(a,b)
# znew = phi*zprev + rt*err
# wgh <- pnorm(b,0,1) - pnorm(a,0,1)
# if (any(is.na(wgh))) # see apf code below for explanation
# {
# #nloglik <- NaN
# nloglik <- Inf
# break
# }
# if (sum(wgh)==0)
# {
# #nloglik <- NaN
# nloglik <- Inf
# break
# }
# wghn <- wgh/sum(wgh)
# ind <- rmultinom(1, N, wghn)
# znew <- rep(znew,ind)
#
# zhat <- phi*znew
# prt[,t] <- zhat
# zprev <- znew
#
# nloglik <- nloglik -2*log(mean(wgh))
# }
#
# nloglik <- nloglik - 2*log(pdf(xt[1],theta1))
#
#
# out = if (is.na(nloglik)) Inf else nloglik
# return(out)
# }
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