R package for probabilistic projections of net migration rate for all countries of the world or for subnational units, using a Bayesian hierarchical model by Azose and Raftery (2015).
The package can be installed from CRAN. For the latest developer version, one can use
library(devtools)
install_github("PPgp/bayesMig")
The two main functions of the package are:
run.mig.mcmc: Runs a Markov Chain Monte Carlo (MCMC) simulation. It results in posterior samples of the model parameters.mig.predict: Using the posterior parameter samples, trajectories of future net migration rates are generated for all countries or given locations.
It can be used for 5-year data or annual data. See ?bayesMig for more info.
Run a simulation that uses default 5-year national historical data from WPP 2019, ranging from 1950 to 2020, and projects to 2100.
library(bayesMig)
sim.dir <- tempfile() # directory to store results into
# Run 4 MCMCs 10,000 iterations each, thinned by 10
# (can take long time; reduce iter for a toy simulation)
m <- run.mig.mcmc(nr.chains = 4, iter = 10000,
thin = 10, output.dir = sim.dir, verbose.iter = 1000)
# Prediction for all countries
pred <- mig.predict(sim.dir = sim.dir, nr.traj = 1000, burnin = 5000)
# Explore results
summary(pred, country = "Germany")
mig.trajectories.plot(pred, country = "Germany", nr.traj = 50)
# Retrieve simulation objects from disk, e.g. at later time
m <- get.mig.mcmc(sim.dir)
pred <- get.mig.prediction(sim.dir)
# Remove simulation directory when not needed
unlink(sim.dir, recursive = TRUE)