Package: RcppSMC 0.2.9

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:Dirk Eddelbuettel [aut, cre], Adam M. Johansen [aut], Leah F. South [aut], Ilya Zarubin [aut]

RcppSMC_0.2.9.tar.gz
RcppSMC_0.2.9.zip(r-4.7)RcppSMC_0.2.9.zip(r-4.6)RcppSMC_0.2.9.zip(r-4.5)
RcppSMC_0.2.9.tgz(r-4.6-x86_64)RcppSMC_0.2.9.tgz(r-4.6-arm64)RcppSMC_0.2.9.tgz(r-4.5-x86_64)RcppSMC_0.2.9.tgz(r-4.5-arm64)
RcppSMC_0.2.9.tar.gz(r-4.7-arm64)RcppSMC_0.2.9.tar.gz(r-4.7-x86_64)RcppSMC_0.2.9.tar.gz(r-4.6-arm64)RcppSMC_0.2.9.tar.gz(r-4.6-x86_64)
RcppSMC_0.2.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RcppSMC/json (API)

# Install 'RcppSMC' in R:
install.packages('RcppSMC', repos = c('https://rcppsmc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rcppsmc/rcppsmc/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

particle-filterrcppsequantial-monte-carloopenblascpp

5.48 score 25 stars 7 scripts 658 downloads 14 exports 3 dependencies

Last updated from:c4770cdd20. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK164
linux-devel-x86_64OK150
source / vignettesOK204
linux-release-arm64OK160
linux-release-x86_64OK132
macos-release-arm64OK139
macos-release-x86_64OK486
macos-oldrel-arm64OK99
macos-oldrel-x86_64OK337
windows-develOK148
windows-releaseOK204
windows-oldrelOK152
wasm-releaseOK168

Exports:blockpfGaussianOptcompareNCestimatesLinRegLinRegLALinRegLA_adaptnonLinPMMHpfLineartBSpfLineartBSOnlinePlotpfNonlinBSRcppSMC.package.skeletonsimGaussiansimGaussianSSMsimLineartsimNonlin

Dependencies:FKFRcppRcppArmadillo