Standard tools to compare and evaluate mortality forecasting methods
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Updated
May 3, 2026 - R
Standard tools to compare and evaluate mortality forecasting methods
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
Forecast mortality using Compositional Data Lee-Carter model - R Package
Improved Mortality Forecasts using Artificial Intelligence.
Lee Carter model and different cross-validation methods for mortality forecasting models, implemented in Python.
Project for the Bayesian Statistics exam at University of Trieste
Mortality rate predictions for Italy in 2020 using Lee-Carter model and Recurrent Neural Networks
Modelling and forecasting adult age-at-death distributions
The Double-Gap Life Expectancy Forecasting Model - R Package
Reproducible benchmark of classical, neural and hybrid mortality forecasting models with actuarial evaluation.
Modelling and forecasting cohort mortality
Modélisation de la mortalité et tarification d’une rente temporaire : Lee-Carter, APC, CBD, validation temporelle et VAP.
R package - Computing mortality rates from tobacco and alcohol related causes. This is a mirror of the code in the private Gitlab repository
Pre-registered calibration audit of ten mortality-forecasting families and seven uncertainty mechanisms across the COVID-19 structural break.
Generalized Additive Forecasting Mortality
Mortality surveillance analysis for 10 African countries (2000–2019): WHO ANACoD data quality assessment, ICD-10 cause-of-death trends, epidemiological transition analysis, and SDG 3.1–3.4 progress tracking. Python pipeline with reproducible outputs.
Mortality Modelling using Generalized Estimating Equations
Python implementations of different mortality modeling techniques (for now Lee-Carter Model)
State-space models for statistical mortality projections
Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model
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