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.
Keywords for this software
References in zbMATH (referenced in 218 articles , 2 standard articles )
Showing results 1 to 20 of 218.
Sorted by year (- Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
- Clemens Schmid; Stephan Schiffels: bleiglas: An R package for interpolation and visualisation of spatiotemporal data with 3D tessellation (2021) not zbMATH
- Francesco Denti: intRinsic: an R package for model-based estimation of the intrinsic dimension of a dataset (2021) arXiv
- Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
- 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)
- Kendal B. Foster, Henrik Singmann: Another Approximation of the First-Passage Time Densities for the Ratcliff Diffusion Decision Model (2021) arXiv
- Mads Lindskou, Søren Højsgaard, Poul Svante Eriksen, Torben Tvedebrink: sparta: Sparse Tables and their Algebra for use in High Dimensional Bayesian Networks (2021) arXiv
- Martin Bladt; Jorge Yslas: matrixdist: An R Package for Inhomogeneous Phase-Type Distributions (2021) arXiv
- Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
- Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
- Rodney Sparapani, Charles Spanbauer, Robert McCulloch: Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package (2021) not zbMATH
- Sam Parsons: splithalf: robust estimates of split half reliability (2021) not zbMATH
- Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
- Thomas Nagler: R-Friendly Multi-Threading in C++ (2021) not zbMATH
- Vasilis Nikolaidis: The nnlib2 library and nnlib2Rcpp R package for implementing neural networks (2021) not zbMATH
- Zhi Zhao, Marco Banterle, Leonardo Bottolo, Sylvia Richardson, Alex Lewin, Manuela Zucknick: BayesSUR: An R package for high-dimensional multivariate Bayesian variable and covariance selection in linear regression (2021) arXiv
- Alexander Lange, Bernhard Dalheimer, Helmut Herwartz, Simone Maxand: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis (2020) not zbMATH
- Andersson, Claes; Mrkvička, Tomáš: Inference for cluster point processes with over- or under-dispersed cluster sizes (2020)
- Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
- Billig Rose, Erica; Roy, Jason A.; Castillo-Neyra, Ricardo; Ross, Michelle E.; Condori-Pino, Carlos; Peterson, Jennifer K.; Naquira-Velarde, Cesar; Levy, Michael Z.: A real-time search strategy for finding urban disease vector infestations (2020)