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 208 articles , 2 standard articles )

Showing results 1 to 20 of 208.
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  1. Francesco Denti: intRinsic: an R package for model-based estimation of the intrinsic dimension of a dataset (2021) arXiv
  2. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  3. Martin Bladt; Jorge Yslas: matrixdist: An R Package for Inhomogeneous Phase-Type Distributions (2021) arXiv
  4. Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
  5. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  6. Rodney Sparapani, Charles Spanbauer, Robert McCulloch: Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package (2021) not zbMATH
  7. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  8. Thomas Nagler: R-Friendly Multi-Threading in C++ (2021) not zbMATH
  9. Andersson, Claes; Mrkvička, Tomáš: Inference for cluster point processes with over- or under-dispersed cluster sizes (2020)
  10. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  11. Buckwar, Evelyn; Tamborrino, Massimiliano; Tubikanec, Irene: Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs (2020)
  12. 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
  13. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  14. da Conceição Amorim, Erick; Mayrink, Vinícius Diniz: Clustering non-linear interactions in factor analysis (2020)
  15. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  16. David B. Dahl: Integration of R and Scala Using rscala (2020) not zbMATH
  17. Diabaté, Modibo; Coquille, Loren; Samson, Adeline: Parameter estimation and treatment optimization in a stochastic model for immunotherapy of cancer (2020)
  18. Diana, Alex; Matechou, Eleni; Griffin, Jim; Johnston, Alison: A hierarchical dependent Dirichlet process prior for modelling bird migration patterns in the UK (2020)
  19. 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
  20. Donald Williams; Joris Mulder: BGGM: Bayesian Gaussian Graphical Models in R (2020) not zbMATH

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