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 188 articles , 2 standard articles )

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  1. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  2. Buckwar, Evelyn; Tamborrino, Massimiliano; Tubikanec, Irene: Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs (2020)
  3. Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang: BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models (2020) not zbMATH
  4. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  5. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  6. David B. Dahl: Integration of R and Scala Using rscala (2020) not zbMATH
  7. Diana, Alex; Matechou, Eleni; Griffin, Jim; Johnston, Alison: A hierarchical dependent Dirichlet process prior for modelling bird migration patterns in the UK (2020)
  8. Dimitrina S. Dimitrova, Vladimir K. Kaishev, Senren Tan: Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous (2020) not zbMATH
  9. Donald Williams; Joris Mulder: BGGM: Bayesian Gaussian Graphical Models in R (2020) not zbMATH
  10. Erik Sverdrup; Ayush Kanodia; Zhengyuan Zhou; Susan Athey; Stefan Wager: policytree: Policy learning via doubly robust empirical welfare maximization over trees (2020) not zbMATH
  11. García-Portugués, Eduardo; Álvarez-Liébana, Javier; Álvarez-Pérez, Gonzalo; González-Manteiga, Wenceslao: Goodness-of-fit tests for functional linear models based on integrated projections (2020)
  12. Gavin Lee, Qing Zhang, Jane W. Liang, Theodore Huang, Christine Choirat, Giovanni Parmigiani, Danielle Braun: PanelPRO: A R package for multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer (2020) arXiv
  13. Giovanna Jona Lasinio; Gianluca Mastrantonio; Mario Santoro: CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data (2020) arXiv
  14. Hao Wang, Diederick Vermetten, Carola Doerr, Thomas Bäck: IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic (2020) arXiv
  15. James P. Howard, II: Phonetic Spelling Algorithm Implementations for R (2020) not zbMATH
  16. Kellen, David; Klauer, Karl Christoph: Selecting amongst multinomial models: an apologia for normalized maximum likelihood (2020)
  17. Kisung You, Changhee Suh: Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation (2020) arXiv
  18. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  19. Markus D. Steiner; Silvia Grieder: EFAtools: An R package with fast and exible implementations of exploratory factor analysis tools (2020) not zbMATH
  20. Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houée-Bigot: Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package (2020) not zbMATH

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