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 102 articles )

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  1. Battauz, Michela; Vidoni, Paolo: A likelihood-based boosting algorithm for factor analysis models with binary data (2022)
  2. Etienne Côme, Nicolas Jouvin : greed: An R Package for Model-Based Clustering by Greedy Maximization of the Integrated Classification Likelihood (2022) arXiv
  3. Victor Freguglia, Nancy Lopes Garcia: Inference Tools for Markov Random Fields on Lattices: The R Package mrf2d (2022) not zbMATH
  4. Wu, Wenbo; Taylor, Jeremy M. G.; Brouwer, Andrew F.; Luo, Lingfeng; Kang, Jian; Jiang, Hui; He, Kevin: Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients (2022)
  5. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  6. Cardona Jiménez, Johnatan; de B. Pereira, Carlos A.: Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: an application to task-based fMRI data analysis (2021)
  7. Corradin, R., Canale, A.,Nipoti, B: BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures (2021) not zbMATH
  8. Francesco Denti: intRinsic: an R package for model-based estimation of the intrinsic dimension of a dataset (2021) arXiv
  9. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  10. Hosszejni, D.; Kastner, G: Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol (2021) not zbMATH
  11. Jason Rumengan, Terry Yue Zhuo, Conrad Sanderson: PyArmadillo: a streamlined linear algebra library for Python (2021) arXiv
  12. Klaus Nordhausen, Markus Matilainen, Jari Miettinen, Joni Virta, Sara Taskinen: Dimension Reduction for Time Series in a Blind Source Separation Context Using R (2021) not zbMATH
  13. Maximilian Leodolter, Claudia Plant, Norbert Brändle: IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping (2021) not zbMATH
  14. Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon: Regularized Ordinal Regression and the ordinalNet R Package (2021) not zbMATH
  15. Peder Bacher, Hjörleifur G. Bergsteinsson, Linde Frölke, Mikkel L. Sørensen, Julian Lemos-Vinasco, Jon Liisberg, Jan Kloppenborg Møller, Henrik Aalborg Nielsen, Henrik Madsen: onlineforecast: An R package for adaptive and recursive forecasting (2021) arXiv
  16. Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
  17. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  18. Rockwood, Nicholas J.: Efficient likelihood estimation of generalized structural equation models with a mix of normal and nonnormal responses (2021)
  19. Thrun, Michael C.; Ultsch, Alfred: Swarm intelligence for self-organized clustering (2021)
  20. 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

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