The package includes routines for state estimate in a linear Gaussian state space model and a simple stochastic volatility model using particle filtering. Parameter inference is also carried out in these models using the particle Metropolis-Hastings algorithm that includes the particle filter to provied an unbiased estimator of the likelihood. The code is developed in the paper and all the details are explained their. This package is meant as a minimal working example of these algorithm and is only meant for educational use and as a start for learning to implement these algorithms on your own.
See the paper in the references below for background information about the package. The main examples in the paper are found in the five example commands and these replicates the results in the paper and are meant for educational purposes to teach the reader about how to implement particle filtering and particle Metropolis-Hastings.
Dahlin, J. & Schoen, T. B. "Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models." pre-print, arXiv:1511.01707, 2015.
example1_lgss, example2_lgss,
example3_sv,example4_sv,
example5_sv