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 168 articles , 2 standard articles )

Showing results 1 to 20 of 168.
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  1. Buckwar, Evelyn; Tamborrino, Massimiliano; Tubikanec, Irene: Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs (2020)
  2. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  3. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  4. David B. Dahl: Integration of R and Scala Using rscala (2020) not zbMATH
  5. Diana, Alex; Matechou, Eleni; Griffin, Jim; Johnston, Alison: A hierarchical dependent Dirichlet process prior for modelling bird migration patterns in the UK (2020)
  6. Giovanna Jona Lasinio; Gianluca Mastrantonio; Mario Santoro: CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data (2020) arXiv
  7. Kisung You, Changhee Suh: Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation (2020) arXiv
  8. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  9. Neeraj Dhanraj Bokde; Gorm Bruun Andersen: ForecastTB - An R Package as a Test-bench for Forecasting Methods Comparison (2020) arXiv
  10. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
  11. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  12. von Schroeder, Jonathan; Dickhaus, Thorsten: Efficient calculation of the joint distribution of order statistics (2020)
  13. Wenchao Ma, Jimmy de la Torre: GDINA: An R Package for Cognitive Diagnosis Modeling (2020) not zbMATH
  14. Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
  15. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  16. Andrew Thomas Jones, Hien Duy Nguyen, Jessica Juanita Bagnall: BoltzMM: an R package for maximum pseudolikelihoodestimation of fully-visible Boltzmann machines (2019) not zbMATH
  17. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  18. Bauer, Verena; Fürlinger, Karl; Kauermann, Göran: A note on parallel sampling in Markov graphs (2019)
  19. Berchuck, Samuel I.; Mwanza, Jean-Claude; Warren, Joshua L.: Diagnosing glaucoma progression with visual field data using a spatiotemporal boundary detection method (2019)
  20. Bhattacharya, Arnab; Wilson, Simon P.; Soyer, Refik: A Bayesian approach to modeling mortgage default and prepayment (2019)

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