countsFun:

# NegBinMA1_GL = function(CountDist,MargParm,MAParm,
#                       n, nsim, no_cores) {
#
# # PURPOSE: Fit Gaussian likelihood to many realizations of synthetic Poisson data with
# #          an underlying MA(1). We want to investigate if having a large lambda
# #          and large sample size reduce the bias in parameter estimates.
# #
# # NOTES:
# #
# # AUTHORS: James Livsey, Stefanos Kechagias, Vladas Pipiras
# #
# # DATE:    April 2020
# #
# # R version 3.5.3
#
# # ---- Load libraries ----
# library(parallel)
# library(doParallel)
# library(countsFun)
#
#
# # ---- setup parameters for Poisson(lam)-AR(1) series ----
#
# initial.param = c(MargParm, MAParm)    # Initial Parameters
#
#
# # Generate all the data and save in a list
# l <- list()
# for(r in 1:nsim){
#   set.seed(r)
#   l[[r]] = sim_negbin_ma(n, MAParm, MargParm[1], MargParm[2] )
# }
#
#
# # initiate and register the cluster
# cl <- makeCluster(no_cores)
# registerDoParallel(cl)
#
# # fit the gaussian log lik using foreach
# all = foreach(index = 1:nsim,
#               .combine = rbind,
#               .packages = c("countsFun")) %dopar%
#   FitGaussianLikNB_MA(initial.param, l[[index]])
#
# stopCluster(cl)
#
# # Prepare results for the plot.
# df = data.frame(matrix(ncol = 11, nrow = nsim))
#
#
# names(df) = c('r.est',
#               'p.est',
#               'theta.est',
#               'estim.method',
#               'n',
#               'theta.true',
#               'theta.se',
#               'r.true',
#               'r.se',
#               'p.true',
#               'p.se' )
#
# df[,1:3] = all[,1:3]
# df[,4]   = 'gaussianLik'
# df[,5]   = n
# df[,6]   = MAParm
# df[,7]   = all[,6]
# df[,8]   = MargParm[1]
# df[,9]   = all[,4]
# df[,10]  = MargParm[2]
# df[,11]  = all[,5]
#
#
#
# return(df)
# }