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 18 articles )

Showing results 1 to 18 of 18.
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  1. Anthony Ebert, Paul Wu, Kerrie Mengersen, Fabrizio Ruggeri: Computationally Efficient Simulation of Queues: The R Package queuecomputer (2017) arXiv
  2. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  3. Jiwoong Kim: A Fast Algorithm for the Coordinate-wise Minimum Distance Estimation (2017) arXiv
  4. John V. Monaco, Malka Gorfine, Li Hsu: General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv (2017) arXiv
  5. Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
  6. Thong Pham, Paul Sheridan, Hidetoshi Shimodaira: PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks (2017) arXiv
  7. Anderson, Eric C.; Ng, Thomas C.: Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation (2016)
  8. Cassese, Alberto; Guindani, Michele; Vannucci, Marina: iBATCGH: integrative Bayesian analysis of transcriptomic and CGH data (2016)
  9. Chambers, John M.: Extending R (2016)
  10. Charlie Wusuo Liu: A Novel Algorithm for the Bounded-Error Multidimensional Subset Sum Problem and its Application to the General-Purpose Knapsack Problem: The FLSSS Package for R (2016) arXiv
  11. Dinov, Ivo D.; Siegrist, Kyle; Pearl, Dennis K.; Kalinin, Alexandr; Christou, Nicolas: Probability \itDistributome: a web computational infrastructure for exploring the properties, interrelations, and applications of probability distributions (2016)
  12. Luukko, P.J.J.; Helske, J.; Räsänen, E.: Introducing libeemd: a program package for performing the ensemble empirical mode decomposition (2016)
  13. Adrien Todeschini, Francois Caron, Marc Fuentes, Pierrick Legrand, Pierre Del Moral: Biips: Software for Bayesian Inference with Interacting Particle Systems (2014) arXiv
  14. Chambers, John M.: Object-oriented programming, functional programming and R (2014)
  15. Temple Lang, Duncan: Enhancing R with advanced compilation tools and methods (2014)
  16. Eddelbuettel, Dirk: Seamless R and C++ integration with Rcpp (2013)
  17. Jochmann, Markus: What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care (2013)
  18. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)