Sequential Monte Carlo Methods in the nimble R Package

Nicholas Michaud, Perry de Valpine, Daniel Turek, Christopher J. Paciorek

Published 2017-03-17Version 1

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms -- specifically, the package allows users to code models in the BUGS language, and it allows users to write algorithms that can be applied to any appropriately-specified BUGS model. In this paper, we introduce nimble's capabilities for state-space model analysis using Sequential Monte Carlo (SMC) techniques. We first provide an overview of state-space models and commonly used SMC algorithms. We then describe how to build a state-space model and conduct inference using existing SMC algorithms within nimble. SMC algorithms within nimble currently include the bootstrap filter, auxiliary particle filter, Liu and West filter, ensemble Kalman filter, and a particle MCMC sampler. These algorithms can be run in R or compiled into C++ for more efficient execution. Examples of applying SMC algorithms to a random walk model and a stochastic volatility model are provided. Finally, we give an overview of how model-generic algorithms are coded within nimble by providing code for a simple SMC algorithm.