KFAS: R Package for Exponential Family State Space Models

statespacer: State Space Modelling in R

bayesian-sgdlm is a Python script for fully Bayesian SGDLMs, treating each node as a VAR( 𝑝) DLM. It leverages decouple–recouple filtering with Variational Bayes and importance sampling to estimate sparse, time-varying cross-lag dependencies (including pandemic dummies) without ever inverting the full multivariate system.

Rank-1 updates by Givens' rotations to solve linear systems which are column-subsampled (selection of dimensions) at each iteration

Simulate and fit dynamic linear models using the Kalman filter, in Fortran

R package for mortality modelling considering dynamic improvement

applications of univariate & multivariate times series with machine and deep learning and dynamic linear modeling

Time series analysis of PM2.5 particles levels with R Markdown utilising Hidden Markov Models (HMM), Dynamic Linear Models (DLM).

Time Series Analysis using Dynami Linear Programming (DLM)

Inflation forecasting during crisis periods using Bayesian Dynamic Linear Models, traditional econometrics, and machine learning. Includes data, code, and comprehensive analysis report.

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