R package for statistical inference using partially observed Markov processes

Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"

R Code to accompany "A Note on Efficient Fitting of Stochastic Volatility Models"

Particle filters, smoothers and sampling algorithms for animal movement modelling, with a focus on passive acoustic telemetry systems.

Bayesian Methods for State Space Models

This repository contains code for the paper `Sequential Monte Carlo algorithms for agent-based models of disease transmission' by Nianqiao (Phyllis) Ju, Jeremy Heng and Pierre Jacob.

R package pmhtutorial available from CRAN.

R package for Bayesian inference with interacting particle systems

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.

Learning resources for users of the `patter` R package

a helper package for pomp

R code supporting Lavender et al. (2025). patter: particle algorithms for animal tracking in R and Julia. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.70029

State-space models for statistical mortality projections

Methods supporting Lavender et al. (2025). Animal tracking with particle algorithms for conservation. bioRxiv. https://doi.org/10.1101/2025.02.13.638042

A fork of Futia et al. (2024), extended with the patter algorithms