countsFun:

# PoisAR1_PF = function(CountDist,MargParm,ARParm,
#                        n, nsim, ParticleSchemes) {
#
#   # PURPOSE: Wrapper that performs simulation and produces Poisson AR(1) Particle filter
#   # estimates for revised Figure 3 (see aarxiv)
#   #
#   #
#   # AUTHORS: Stefanos Kechagias, James Livsey, Vladas Pipiras
#   #
#   # DATE:    April 2020
#   #
#   # R version 3.6.3
#
#
#   # load PF likelihood function and other necessary functions from UNC cluster directory
#
#   #source('C:/Users/Stef/Desktop/countsFun/R/LikSISGenDist_ARp_Res.R')
#   #source('C:/Users/Stef/Desktop/countsFun/R/LikSIS_ARpGenDist_functions.R')
#
#   # load necessary libraries.
#   library(itsmr)
#   library(FitAR)
#   library(foreach)
#   library(doParallel)
#   library(tictoc)
#
#   # Simulation scheme details
#   PhiSign       = ifelse(ARParm > 0, 'Pos', 'Neg')   # SIGN OF ar(1) param
#   ARorder       = length(ARParm)                     # AR parameters
#   nfit          = 1                                  # number of times that we fit the same realization
#   initial.param = c(MargParm, ARParm)                # Initial PArameters
#   no_cores      = detectCores() - 1                  # Select the number of cores
#   ##-------------------------------------------------------------------------------------------------#
#
#   # generate all the realizations and save in a list
#   l <- list()
#   for (i in 1:nsim) {
#     set.seed(i)
#     l[[i]] = sim_pois_ar(n, ARParm, MargParm)
#   }
#
#   t0 = tic()
#   # initiate and register the cluster
#   cl <- makeCluster(no_cores)
#
#   #clusterSetRNGStream(cl, 1001) #make the bootstrapping exactly the same as above to equate computation time
#   registerDoParallel(cl)
#
#   # run foreach
#   all = foreach(index = 1:nsim,
#                 .combine = rbind,
#                 .packages = "FitAR")  %dopar%  {
#                   FitMultiplePF(initial.param, l[[index]], CountDist, nfit, ParticleSchemes)
#                 }
#
#   stopCluster(cl)
#   toc(t0)
#
#
#   # Prepare results for the plot.
#   df = data.frame(matrix(ncol = 8, nrow = nsim))
#
#   #Create columns lam.est, phi.est, estim.method, n, phi, phi.se, lam, lam.se
#   names(df) = c(
#     'lam.est',
#     'phi.est',
#     'estim.method',
#     'n',
#     'phi.true',
#     'phi.se',
#     'lam.true',
#     'lam.se'
#   )
#
#   df[, 1:2] = all[, 1:2]
#   df[, 3] = 'particle'
#   df[, 4] = n
#   df[, 5] = ARParm
#   df[, 6] = all[, 4]
#   df[, 7] = MargParm
#   df[, 8] = all[, 3]
#
#   return(df)
# }