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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rcppsmc/rcppsmc/issues
- radiata - Radiata pine dataset
Last updated 2 years agofrom:b807d20659. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 20 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 20 2024 |
Exports:blockpfGaussianOptcompareNCestimatesLinRegLinRegLALinRegLA_adaptnonLinPMMHpfLineartBSpfLineartBSOnlinePlotpfNonlinBSRcppSMC.package.skeletonsimGaussiansimGaussianSSMsimLineartsimNonlin
Dependencies:FKFRcppRcppArmadillo
Readme and manuals
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 |
