Learn R Programming

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, doi:10.18637/jss.v030.i06).

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.

More Examples

See the packages ToyPackage, RickerExample, SVmodelRcppSMC, and demo repository SVmodelExamples next to this one in the RcppSMC organization.

Help

For support and discussion please make us of the rcppsmc mailing list.

Authors

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

License

GPL (>= 2)

Copy Link

Version

Install

install.packages('RcppSMC')

Monthly Downloads

293

Version

0.2.7

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Last Published

March 22nd, 2023

Functions in RcppSMC (0.2.7)

simNonlin

Simulates from a simple nonlinear state space model.
blockpfGaussianOpt

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

Conditional Sequential Monte Carlo Examples
RcppSMC.package.skeleton

Create a skeleton for a new package that intends to use RcpSMCp
LinReg

Simple Linear Regression
nonLinPMMH

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

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

Particle Filter Example
radiata

Radiata pine dataset (linear regression example)