期刊
JOURNAL OF STATISTICAL SOFTWARE
卷 100, 期 3, 页码 1-39出版社
JOURNAL STATISTICAL SOFTWARE
DOI: 10.18637/jss.v100.i03
关键词
particle filtering; sequential Monte Carlo; auxiliary particle filter; IF2 iterated filtering; ensemble Kalman filter; particle MCMC; R; nimbleSMC; nimble
资金
- NSF [DBI-1147230, ACI-1550488]
nimble is an R package that provides flexibility in model specification and allows users to program model-generic algorithms. nimbleSMC is an extension package that contains algorithms for state-space model analysis using SMC techniques.
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 appropriate model. In this paper, we introduce the nimbleSMC R package. nimbleSMC contains algorithms for state-space model analysis using sequential Monte Carlo (SMC) techniques that are built using nimble. We first provide an overview of state-space models and commonly-used SMC algorithms. We then describe how to build a state-space model in nimble and conduct inference using existing SMC algorithms within nimbleSMC. SMC algorithms within nimbleSMC currently include the bootstrap filter, auxiliary particle filter, ensemble Kalman filter, IF2 method of iterated filtering, and a particle Markov chain Monte Carlo (MCMC) sampler. These algorithms can be run in R or compiled into C++ for more efficient execution. Examples of applying SMC algorithms to linear autoregressive models 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. This illustrates how users can easily extend nimble's SMC methods in high-level code.
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