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 273 articles , 2 standard articles )

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  1. Battauz, Michela; Vidoni, Paolo: A likelihood-based boosting algorithm for factor analysis models with binary data (2022)
  2. Cao, J., Genton, M. G., Keyes, D. E., Turkiyyah, G. M. : tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-Rank Methods in R (2022) not zbMATH
  3. C. Marques F., Paulo; Graziadei, Helton; Lopes, Hedibert F.: Bayesian generalizations of the integer-valued autoregressive model (2022)
  4. Coube-Sisqueille, Sébastien; Liquet, Benoît: Improving performances of MCMC for nearest neighbor Gaussian process models with full data augmentation (2022)
  5. Guinness, Joseph: Nonparametric spectral methods for multivariate spatial and spatial-temporal data (2022)
  6. Park, Jaewoo; Jin, Ick Hoon; Schweinberger, Michael: Bayesian model selection for high-dimensional Ising models, with applications to educational data (2022)
  7. Victor Freguglia, Nancy Lopes Garcia: Inference Tools for Markov Random Fields on Lattices: The R Package mrf2d (2022) not zbMATH
  8. Youjiao Yu: mixR: An R package for Finite Mixture Modeling for Both Raw and Binned Data (2022) not zbMATH
  9. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  10. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  11. Clemens Schmid; Stephan Schiffels: bleiglas: An R package for interpolation and visualisation of spatiotemporal data with 3D tessellation (2021) not zbMATH
  12. Corradin, R., Canale, A.,Nipoti, B: BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures (2021) not zbMATH
  13. da Silva, Natalia; Cook, Dianne; Lee, Eun-Kyung: A projection pursuit forest algorithm for supervised classification (2021)
  14. David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
  15. Director, Hannah M.; Raftery, Adrian E.; Bitz, Cecilia M.: Probabilistic forecasting of the Arctic sea ice edge with contour modeling (2021)
  16. Francesco Denti: intRinsic: an R package for model-based estimation of the intrinsic dimension of a dataset (2021) arXiv
  17. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  18. Holbrook, Andrew J.; Lemey, Philippe; Baele, Guy; Dellicour, Simon; Brockmann, Dirk; Rambaut, Andrew; Suchard, Marc A.: Massive parallelization boosts big Bayesian multidimensional scaling (2021)
  19. Holbrook, Andrew J.; Loeffler, Charles E.; Flaxman, Seth R.; Suchard, Marc A.: Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data (2021)
  20. Hosszejni, D.; Kastner, G: Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol (2021) not zbMATH

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