Package: RcppSMC 0.2.7
Dirk Eddelbuettel
RcppSMC: Rcpp Bindings for Sequential Monte Carlo
R access to the Sequential Monte Carlo Template Classes by Johansen <doi:10.18637/jss.v030.i06> is provided. 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:
RcppSMC_0.2.7.tar.gz
RcppSMC_0.2.7.tar.gz(r-4.5-noble)RcppSMC_0.2.7.tar.gz(r-4.4-noble)
RcppSMC_0.2.7.tgz(r-4.4-emscripten)RcppSMC_0.2.7.tgz(r-4.3-emscripten)
RcppSMC.pdf |RcppSMC.html✨
RcppSMC/json (API)
NEWS
| # Install 'RcppSMC' in R: |
| install.packages('RcppSMC', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/rcppsmc/rcppsmc/issues
- radiata - Radiata pine dataset
Last updated 2 years agofrom:b807d20659. Checks:1 OK, 2 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 20 2025 |
| R-4.5-linux-x86_64 | NOTE | Mar 20 2025 |
| R-4.4-linux-x86_64 | NOTE | Mar 20 2025 |
Exports:blockpfGaussianOptcompareNCestimatesLinRegLinRegLALinRegLA_adaptnonLinPMMHpfLineartBSpfLineartBSOnlinePlotpfNonlinBSRcppSMC.package.skeletonsimGaussiansimGaussianSSMsimLineartsimNonlin
Dependencies:FKFRcppRcppArmadillo
Citation
To cite package ‘RcppSMC’ in publications use:
Eddelbuettel D, Johansen AM, South LF, Zarubin I (2023). RcppSMC: Rcpp Bindings for Sequential Monte Carlo. R package version 0.2.7, https://CRAN.R-project.org/package=RcppSMC.
ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.
Corresponding BibTeX entry:
@Manual{,
title = {RcppSMC: Rcpp Bindings for Sequential Monte Carlo},
author = {Dirk Eddelbuettel and Adam M. Johansen and Leah F. South
and Ilya Zarubin},
year = {2023},
note = {R package version 0.2.7},
url = {https://CRAN.R-project.org/package=RcppSMC},
}
Readme and manuals
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)
Help Manual
| Help page | Topics |
|---|---|
| Block Sampling Particle Filter (Linear Gaussian Model; Optimal Proposal) | blockpfGaussianOpt simGaussian |
| Conditional Sequential Monte Carlo Examples | compareNCestimates kalmanFFBS simGaussianSSM |
| Simple Linear Regression | LinReg LinRegLA LinRegLA_adapt |
| Particle marginal Metropolis-Hastings for a non-linear state space model. | nonLinPMMH |
| Particle Filter Example | pfLineartBS pfLineartBSOnlinePlot simLineart |
| Nonlinear Bootstrap Particle Filter (Univariate Non-Linear State Space Model) | pfNonlinBS |
| Radiata pine dataset (linear regression example) | radiata |
| Create a skeleton for a new package that intends to use RcpSMCp | RcppSMC.package.skeleton |
| Simulates from a simple nonlinear state space model. | simNonlin |
