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.
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References in zbMATH (referenced in 55 articles , 1 standard article )
Showing results 1 to 20 of 55.
Sorted by year (- 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
- Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
- Jeremy Yee: rlsm: R package for least squares Monte Carlo (2018) arXiv
- Anthony Ebert, Paul Wu, Kerrie Mengersen, Fabrizio Ruggeri: Computationally Efficient Simulation of Queues: The R Package queuecomputer (2017) arXiv
- Antony Overstall, David Woods, Maria Adamou: acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange (2017) arXiv
- 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)
- Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
- Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
- Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang: BeSS: An R Package for Best Subset Selection in Linear, Logistic and CoxPH Models (2017) arXiv
- Culpepper, Steven Andrew; Balamuta, James Joseph: A hierarchical model for accuracy and choice on standardized tests (2017)
- Gentle, James E.: Matrix algebra. Theory, computations and applications in statistics (2017)
- He Zhao and Graham Williams and Joshua Huang: wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests (2017)
- Jianxin Pan, Yi Pan: jmcm: An R Package for Joint Mean-Covariance Modeling of Longitudinal Data (2017)
- Jiwoong Kim: A Fast Algorithm for the Coordinate-wise Minimum Distance Estimation (2017) arXiv
- John V. Monaco, Malka Gorfine, Li Hsu: General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv (2017) arXiv
- Jouni Helske: KFAS: Exponential Family State Space Models in R (2017)
- Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
- Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu: cqrReg: An R Package for Quantile and Composite Quantile Regression and Variable Selection (2017) arXiv
- Matthias Templ and Bernhard Meindl and Alexander Kowarik and Olivier Dupriez: Simulation of Synthetic Complex Data: The R Package simPop (2017)
- Michael Braun: sparseHessianFD: An R Package for Estimating Sparse Hessian Matrices (2017)