| Type: | Package |
| Title: | Interface to the 'C++' Library 'Pf' |
| Version: | 1.0.1 |
| Description: | Builds and runs 'c++' code for classes that encapsulate state space model, particle filtering algorithm pairs. Algorithms include the Bootstrap Filter from Gordon et al. (1993) <doi:10.1049/ip-f-2.1993.0015>, the generic SISR filter, the Auxiliary Particle Filter from Pitt et al (1999) <doi:10.2307/2670179>, and a variety of Rao-Blackwellized particle filters inspired by Andrieu et al. (2002) <doi:10.1111/1467-9868.00363>. For more details on the 'c++' library 'pf', see Brown (2020) <doi:10.21105/joss.02599>. |
| License: | GPL (≥ 3) |
| Imports: | inline (≥ 0.3.19), methods, rstudioapi (≥ 0.13) |
| RoxygenNote: | 7.2.1 |
| Encoding: | UTF-8 |
| Suggests: | BH, Rcpp (≥ 1.0.11), RcppEigen, knitr (≥ 1.39), rmarkdown (≥ 2.23) |
| VignetteBuilder: | knitr, rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2023-12-08 15:44:06 UTC; taylor |
| Author: | Taylor Brown 
[aut, cre] |
| Maintainer: | Taylor Brown <trb5me@virginia.edu> |
| Repository: | CRAN |
| Date/Publication: | 2023-12-08 17:40:02 UTC |
Build c++ particle filtering code for your R session.
Description
Build c++ particle filtering code for your R session.
Usage
buildModelFuncs(myDir, modelName, verbose = FALSE)
Arguments
myDir |
directory with your three code files (i.e. model header, model source and export code) |
modelName |
your model name. Must be in all lowercase, and be a substring of the above-mentioned filenames |
verbose |
logical and passed in to inline::cxxfunction() |
Value
an Rcpp Module object
Examples
## Not run:
# compile everything from scratch
svol_lev <- buildModelFuncs("~/Desktop", "svol_leverage")
# then use your model's log-likelihood and filtering functions
svol_lev$svol_leverage_bswc_approx_LL(rnorm(100), c(.9, 0.0, 1.0, -.2))
svol_lev$svol_leverage_bswc_approx_filt(rnorm(100), c(.9, 0.0, 1.0, -.2))
## End(Not run)
Create c++ template files for bootstrap filters (with or without covariates), auxiliary particle filters, sequential importance sampling with resampling filters, or Rao-Blackwellized/Marginal particle filters.
Description
Create c++ template files for bootstrap filters (with or without covariates), auxiliary particle filters, sequential importance sampling with resampling filters, or Rao-Blackwellized/Marginal particle filters.
Usage
createPFCPPTemplates(modname, pfAlgo, fileDir, openNow = TRUE)
Arguments
modname |
name of model in all lowercase |
pfAlgo |
Either "BSF", "APF", "BSWC", "SISR", "RBPFHMM", or "RBPFKALMAN" |
fileDir |
where to save files. Not saved if NULL (but three files are returned in list). |
openNow |
TRUE if you want to open this now in RStudio. Ignored if fileDir is NULL. |
Value
NULL if saving files, otherwise a list with three character vectors
Examples
# return in list of character strings
createPFCPPTemplates("coolmod", "BSF", fileDir = NULL)
## Not run:
# save three files to Desktop, and
# begin editing them in rstudio IDE
createPFCPPTemplates("coolmod", "BSF", fileDir = "~/Desktop/")
## End(Not run)