Forecast mortality using Compositional Data Lee-Carter model - R Package
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Updated
Dec 4, 2018 - R
Forecast mortality using Compositional Data Lee-Carter model - R Package
Standard tools to compare and evaluate mortality forecasting methods
Modelling and forecasting cohort mortality
Project for the Bayesian Statistics exam at University of Trieste
Improved Mortality Forecasts using Artificial Intelligence.
The Double-Gap Life Expectancy Forecasting Model - R Package
Lee Carter model and different cross-validation methods for mortality forecasting models, implemented in Python.
R package - Computing mortality rates from tobacco and alcohol related causes. This is a mirror of the code in the private Gitlab repository
CoMoMo combines multiple mortality forecasts using different model combinations. See more from the paper here https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3823511
Generalized Additive Forecasting Mortality
Mortality Modelling using Generalized Estimating Equations
State-space models for statistical mortality projections
Modelling and forecasting adult age-at-death distributions
Modelling and forecasting age-at-death distributions
Python implementations of different mortality modeling techniques (for now Lee-Carter Model)
Mortality rate predictions for Italy in 2020 using Lee-Carter model and Recurrent Neural Networks
Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model
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