Bayesian mortality modeling

Fit and forecast mortality models

apc_stan()

Bayesian Age-Period-Cohort model with 'Stan'

cbd_stan()

Bayesian Cairns-Blake-Dowd (CBD) model with Stan

lc_stan()

Bayesian Lee-Carter with Stan

m6_stan()

Bayesian M6 model with Stan

rh_stan()

Bayesian Renshaw-Haberman model with Stan

Bayesian model averaging

mortality_weights()

Model averaging/weighting via future-out stacking or pseudo-BMA weighting

compute_weights_BMA()

Compute the model evidence/marginal likelihood via bridge sampling (or via the harmonic mean estimator if bridge sampling fails)

Functions for simulation study

Useful functions to conduct simulation study

sim_death_apc()

Simulation of death counts from the Age-Period-Cohort mortality model

sim_death_cbd()

Simulation of death counts from the CBD model

sim_death_lc()

Simulation of death counts from the Lee-Carter mortality model

sim_death_m6()

Simulation of death counts from the M6 model

sim_death_mix_cbd_rh()

Simulation of death counts from a hybrid model that averages the mortality rates from the cbd and rh models

sim_death_rh()

Simulation of death counts from the Renshaw-Haberman mortality model

sim_mortality_data()

Simulation of mortality data from various models

Plotting functions

boxplot_post_dist()

Boxplot for the posterior distribution

forecasting_plot()

Fanplot for the mortality predictions

Data

FRMaleData

Deaths and Exposures Data of French Males

Functions for statistical analysis

extract_map()

Function to get the a posterior means of the parameters based on a stanfit object

fit_mo_mo()

Wrapper function to fit and forecast mortality models

Data collection

load_HMD_data()

Download mortality data from the Human Mortality Database.

Package description

StanMoMo-package

The 'StanMoMo' package.