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

Improved Mortality Forecasts using Artificial Intelligence.

Forecast mortality using Compositional Data Lee-Carter model - R Package

Project for the Bayesian Statistics exam at University of Trieste

Lee Carter model and different cross-validation methods for mortality forecasting models, implemented in Python.

Mortality rate predictions for Italy in 2020 using Lee-Carter model and Recurrent Neural Networks

Modelling and forecasting adult age-at-death distributions

Modelling and forecasting cohort mortality

The Double-Gap Life Expectancy Forecasting Model - R Package

R package - Computing mortality rates from tobacco and alcohol related causes. This is a mirror of the code in the private Gitlab repository

Generalized Additive Forecasting Mortality

Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model

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)

Modelling and forecasting age-at-death distributions

Using ML Models for predicting the top 5 factors for premature mortality using the County Health Rankings dataset

State-space models for statistical mortality projections