R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R

statespacer: State Space Modelling in R

A new multi-class ensemble classification algorithm based on Kalman filters

Comparison of Stochastic Forecasting Methods

Time series modelling with extended regression SARIMA models

Undergraduate Final Project on behalf of Radisha Fanni Sianti

Statistical analysis of affine mortality models. Implementation of univariate Kalman Filter based routines for the estimation, goodness of fit assessment and projection of affine mortality models

R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel.

This repository provides code in R reproducing examples of the states space models presented in book "An Introduction to State Space Time Series Analysis" by J.J.F. Commandeur and S.J. Koopman.

Project to explore state space model inference including Kalman filters and hidden Markov models.

R and C++ codes that can be used to replicate the empirical results obtained in the paper "Time-varying state correlations in state space models and their estimation via indirect inference" by Caterina Schiavoni, Siem Jan Koopman, Franz Palm, Stephan Smeekes and Jan van den Brakel.

Shiny app. Useful to explore trends in time series, particularly designed for KPIs estimated from survey data. Author: Carlos Omar Pardo Gomez.

An R Package for the functions generate_filaments() and crescent_kf(), for use in SMLM (DNA-PAINT) image segmentation of filamentous structures, such as microtubules.

R package to fix gaps in time series data

"COVID-19 Analyzer" web application for statistical analysis of COVID-19 related data. This software was created as part of my thesis engineering project.