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# rm(list=ls(all.names=TRUE))
#
# # source('simulate_poisson.R')
#
# R=.95
# n=50
# s=1
# rho=1
# t_max=1e3
#
# Nrep <- 200
# Rs <- rep(NA,Nrep)
#
# for (k in 1:Nrep){
#
# print(k)
#
# Sim_I <- simulate_poisson(R,n,s,rho,t_max)
#
# # hist(Sim_I$true_size)
# # mean(Sim_I$true_size)
#
# y_obs <- Sim_I$observed_size
# # source('alpha_poisson.R')
# # source('element_Lhood_poisson.R')
# # source('f_z_poiss.R')
# # source('proba_ext.R')
# # source('proba_observation.R')
# # source('profile_likelihood.R')
# # source('R_eff_poisson.R')
# # source('theta_max_likelihood.R')
# # source('import.R')
#
#
# profile <- profile_likelihood(y_obs = y_obs,
# rho = rho,
# accuracy = 0.01,
# max_R = 20)
# # plot(profile$theta,profile$Likelihood)
#
# R_estimate <- theta_max_likelihood(theta = profile$theta,
# likelihood = profile$Likelihood,
# threshold_CI = 0.95)
#
# c(R,R_estimate$theta_max_likelihood,R_estimate$lower_theta,R_estimate$upper_theta)
#
# Rs[k] <- R_estimate$theta_max_likelihood
#
# # import<-import(y_obs = y_obs,
# # rho = rho,
# # profile = profile,
# # threshold_z = 1e3,
# # threshold_import = 1e3,
# # CI = 0.95)
# #
# # c(n,length(y_obs)+import$theta_max_likelihood,
# # length(y_obs)+import$lower_theta,
# # length(y_obs)+import$upper_theta)
#
#
# }
# hist(R-Rs)
# sum(R>Rs)/Nrep
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