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

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

R package for mortality modelling considering dynamic improvement

Time Series Analysis using Dynami Linear Programming (DLM)

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

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

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

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