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 107 articles , 2 standard articles )

Showing results 1 to 20 of 107.
Sorted by year (citations)

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  1. Bhattacharya, Arnab; Wilson, Simon P.; Soyer, Refik: A Bayesian approach to modeling mortgage default and prepayment (2019)
  2. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  3. Jonathon Love; Ravi Selker; Maarten Marsman; Tahira Jamil; Damian Dropmann; Josine Verhagen; Alexander Ly; Quentin Gronau; Martin Šmíra; Sacha Epskamp; Dora Matzke; Anneliese Wild; Patrick Knight; Jeffrey Rouder; Richard Morey; Eric-Jan Wagenmakers: JASP: Graphical Statistical Software for Common Statistical Designs (2019) not zbMATH
  4. 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)
  5. Bercin, Kutalmis M.; Xie, Zheng-Tong; Turnock, Stephen R.: Exploration of digital-filter and forward-stepwise synthetic turbulence generators and an improvement for their skewness-kurtosis (2018)
  6. Bilgrau, Anders Ellern; Brøndum, Rasmus Froberg; Eriksen, Poul Svante; Dybkær, Karen; Bøgsted, Martin: Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma (2018)
  7. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  8. 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
  9. Eun-Kyung Lee: PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees (2018) not zbMATH
  10. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  11. Jeremy Yee: rlsm: R package for least squares Monte Carlo (2018) arXiv
  12. John Monaco; Malka Gorfine; Li Hsu: General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv (2018) not zbMATH
  13. Kahle, David; Yoshida, Ruriko; Garcia-Puente, Luis: Hybrid schemes for exact conditional inference in discrete exponential families (2018)
  14. 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)
  15. Leopoldo Catania; Nima Nonejad: Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package (2018) not zbMATH
  16. Marchese, Scott; Diao, Guoqing: Joint regression analysis of mixed-type outcome data via efficient scores (2018)
  17. Mathieu Carmassi; Pierre Barbillon; Matthieu Chiodetti; Merlin Keller; Eric Parent: CaliCo: a R package for Bayesian calibration (2018) arXiv
  18. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  19. Natalia da Silva, Eun-Kyung Lee, Di Cook: A Projection Pursuit Forest Algorithm for Supervised Classification (2018) arXiv
  20. Parisi, Antonio; Liseo, B.: Objective Bayesian analysis for the multivariate skew-$t$ model (2018)

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