Rcpp

Rcpp: Seamless R and C++ Integration. The Rcpp package provides R functions as well as a C++ library which facilitate the integration of R and C++. R data types (SEXP) are matched to C++ objects in a class hierarchy. All R types are supported (vectors, functions, environment, etc ...) and each type is mapped to a dedicated class. For example, numeric vectors are represented as instances of the Rcpp::NumericVector class, environments are represented as instances of Rcpp::Environment, functions are represented as Rcpp::Function, etc ... The ”Rcpp-introduction” vignette provides a good entry point to Rcpp. Conversion from C++ to R and back is driven by the templates Rcpp::wrap and Rcpp::as which are highly flexible and extensible, as documented in the ”Rcpp-extending” vignette. Rcpp also provides Rcpp modules, a framework that allows exposing C++ functions and classes to the R level. The ”Rcpp-modules” vignette details the current set of features of Rcpp-modules. Rcpp includes a concept called Rcpp sugar that brings many R functions into C++. Sugar takes advantage of lazy evaluation and expression templates to achieve great performance while exposing a syntax that is much nicer to use than the equivalent low-level loop code. The ”Rcpp-sugar” vignette gives an overview of the feature. Rcpp attributes provide a high-level syntax for declaring C++ functions as callable from R and automatically generating the code required to invoke them. Attributes are intended to facilitate both interactive use of C++ within R sessions as well as to support R package development. Attributes are built on top of Rcpp modules and their implementation is based on previous work in the inline package. Many examples are included, and around 891 unit tests in 430 unit test functions provide additional usage examples. An earlier version of Rcpp, containing what we now call the ’classic Rcpp API’ was written during 2005 and 2006 by Dominick Samperi. This code has been factored out of Rcpp into the package RcppClassic, and it is still available for code relying on the older interface. New development should always use this Rcpp package instead. Additional documentation is available via the paper by Eddelbuettel and Francois (2011, JSS) paper and the book by Eddelbuettel (2013, Springer); see ’citation(”Rcpp”)’ for details.


References in zbMATH (referenced in 95 articles , 2 standard articles )

Showing results 1 to 20 of 95.
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  1. Augustine, Ben C.; Royle, J. Andrew; Kelly, Marcella J.; Satter, Christopher B.; Alonso, Robert S.; Boydston, Erin E.; Crooks, Kevin R.: Spatial capture-recapture with partial identity: an application to camera traps (2018)
  2. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018)
  3. Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang: SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting (2018) arXiv
  4. Eun-Kyung Lee: PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees (2018)
  5. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  6. Jeremy Yee: rlsm: R package for least squares Monte Carlo (2018) arXiv
  7. John Monaco; Malka Gorfine; Li Hsu: General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv (2018)
  8. Kahle, David; Yoshida, Ruriko; Garcia-Puente, Luis: Hybrid schemes for exact conditional inference in discrete exponential families (2018)
  9. Lee, Xing Ju; Hainy, Markus; McKeone, James P.; Drovandi, Christopher C.; Pettitt, Anthony N.: ABC model selection for spatial extremes models applied to south Australian maximum temperature data (2018)
  10. Leopoldo Catania; Nima Nonejad: Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package (2018)
  11. Marchese, Scott; Diao, Guoqing: Joint regression analysis of mixed-type outcome data via efficient scores (2018)
  12. Mathieu Carmassi; Pierre Barbillon; Matthieu Chiodetti; Merlin Keller; Eric Parent: CaliCo: a R package for Bayesian calibration (2018) arXiv
  13. Natalia da Silva, Eun-Kyung Lee, Di Cook: A Projection Pursuit Forest Algorithm for Supervised Classification (2018) arXiv
  14. Richard Beare; Bradley Lowekamp; Ziv Yaniv: Image Segmentation, Registration and Characterization in R with SimpleITK (2018)
  15. Ron Wehrens; Johannes Kruisselbrink: Flexible Self-Organizing Maps in kohonen 3.0 (2018)
  16. Venelin Mitov; Krzysztof Bartoszek; Georgios Asimomitis; Tanja Stadler: Fast likelihood evaluation for multivariate phylogenetic comparative methods: the PCMBase R package (2018) arXiv
  17. Anthony Ebert, Paul Wu, Kerrie Mengersen, Fabrizio Ruggeri: Computationally Efficient Simulation of Queues: The R Package queuecomputer (2017) arXiv
  18. Antony Overstall, David Woods, Maria Adamou: acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange (2017) arXiv
  19. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Mayoral, Asunción; Morales, Javier; Barber, Antoni: Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices (2017)
  20. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)

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