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RcppSMC

Rcpp Bindings for Sequential Monte Carlo

Summary

This package provides R with access to the Sequential Monte Carlo Template Classes by Johansen (Journal of Statistical Software, 2009, v30, i6).

At present, four additional examples have been added, and the first example from the JSS paper has been extended. Further integration and extensions are planned.

Authors

Dirk Eddelbuettel, Adam M. Johansen and Leah F. South

License

GPL (>= 2)

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Install

install.packages('RcppSMC')

Monthly Downloads

316

Version

0.2.0

License

GPL (>= 2)

Maintainer

Dirk Eddelbuettel

Last Published

August 28th, 2017

Functions in RcppSMC (0.2.0)

simNonlin

Simulates from a simple nonlinear state space model.
LinReg

Simple Linear Regression
blockpfGaussianOpt

Block Sampling Particle Filter (Linear Gaussian Model; Optimal Proposal)
nonLinPMMH

Particle marginal Metropolis-Hastings for a non-linear state space model.
pfLineartBS

Particle Filter Example
pfNonlinBS

Nonlinear Bootstrap Particle Filter (Univariate Non-Linear State Space Model)
radiata

Radiata pine dataset (linear regression example)