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"

Bayesian Methods for State Space Models

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

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.

a helper package for pomp

Sequential Monte Carlo algorithms for the Bayesian Mallows model.

Learning resources for users of the `patter` R package

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

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

State-space models for statistical mortality projections

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