RcppArmadillo

RcppArmadillo: Rcpp integration for Armadillo templated linear algebra library. R and Armadillo integration using Rcpp Armadillo is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. A delayed evaluation approach is employed (during compile time) to combine several operations into one, and to reduce (or eliminate) the need for temporaries. This is accomplished through recursive templates and template meta-programming. This library is useful if C++ has been decided as the language of choice (due to speed and/or integration capabilities), rather than another language. The RcppArmadillo package includes the header files from the templated Armadillo library. Thus users do not need to install Armadillo itself in order to use RcppArmadillo. This Armadillo integration provides a nice illustration of the capabilities of the Rcpp package for seamless R and C++ integration. Armadillo is licensed under the MPL 2.0, while RcppArmadillo (the Rcpp bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of Rcpp.


References in zbMATH (referenced in 81 articles )

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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. Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
  4. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  5. da Conceição Amorim, Erick; Mayrink, Vinícius Diniz: Clustering non-linear interactions in factor analysis (2020)
  6. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  7. Derek Beaton: Generalized eigen, singular value, and partial least squares decompositions: The GSVD package (2020) arXiv
  8. Feng, Xiangnan; Li, Tengfei; Song, Xinyuan; Zhu, Hongtu: Bayesian scalar on image regression with nonignorable nonresponse (2020)
  9. Flagg, Kenneth A.; Hoegh, Andrew; Borkowski, John J.: Modeling partially surveyed point process data: inferring spatial point intensity of geomagnetic anomalies (2020)
  10. Gavin Lee, Qing Zhang, Jane W. Liang, Theodore Huang, Christine Choirat, Giovanni Parmigiani, Danielle Braun: PanelPRO: A R package for multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer (2020) arXiv
  11. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  12. Giovanna Jona Lasinio; Gianluca Mastrantonio; Mario Santoro: CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data (2020) arXiv
  13. Jauslin, Raphaël; Tillé, Yves: Spatial spread sampling using weakly associated vectors (2020)
  14. Kisung You, Changhee Suh: Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation (2020) arXiv
  15. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  16. Markus D. Steiner; Silvia Grieder: EFAtools: An R package with fast and exible implementations of exploratory factor analysis tools (2020) not zbMATH
  17. Moores, Matthew; Nicholls, Geoff; Pettitt, Anthony; Mengersen, Kerrie: Scalable Bayesian inference for the inverse temperature of a hidden Potts model (2020)
  18. Nolan, Tui H.; Menictas, Marianne; Wand, Matt P.: Streamlined variational inference with higher level random effects (2020)
  19. Oskar Laverny: Empirical and non-parametric copula models with the cort R package (2020) not zbMATH
  20. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)

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