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Rcpp: Seamless R and C++ Integration

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  • Dirk Eddelbuettel
  • Romain Francois
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    Abstract

    The Rcpp package simplifies integrating C++ code with R. It provides a consistent C++ class hierarchy that maps various types of R objects (vectors, matrices, functions, environments, . . . ) to dedicated C++ classes. Object interchange between R and C++ is managed by simple, flexible and extensible concepts which include broad support for C++ Standard Template Library idioms. C++ code can both be compiled, linked and loaded on the fly, or added via packages. Flexible error and exception code handling is provided. Rcpp substantially lowers the barrier for programmers wanting to combine C++ code with R.

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    File URL: http://www.jstatsoft.org/v40/i08/paper
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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 40 ()
    Issue (Month): i08 ()
    Pages:

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    Handle: RePEc:jss:jstsof:40:i08

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    Web page: http://www.jstatsoft.org/

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    Cited by:
    1. Jochmann, Markus, 2009. "What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care," SIRE Discussion Papers 2009-54, Scottish Institute for Research in Economics (SIRE).
    2. Cheng, Ching-Wei & Hung, Ying-Chao & Balakrishnan, Narayanaswamy, 2014. "Generating beta random numbers and Dirichlet random vectors in R: The package rBeta2009," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1011-1020.
    3. Julien Boelaert, 2013. "A Neural Network Demand System," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917810, HAL.
    4. Bivand, Roger, 2011. "Geocomputation and open source software: components and software stacks," Discussion Paper Series in Economics 23/2011, Department of Economics, Norwegian School of Economics.
    5. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
    6. Julien Boelaert, 2013. "A Neural Network Demand System," Documents de travail du Centre d'Economie de la Sorbonne 13081, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Augustyniak, Maciej, 2014. "Maximum likelihood estimation of the Markov-switching GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 61-75.
    8. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.

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