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.
Keywords for this software
References in zbMATH (referenced in 13 articles )
Showing results 1 to 13 of 13.
Sorted by year (- Anthony Ebert, Paul Wu, Kerrie Mengersen, Fabrizio Ruggeri: Computationally Efficient Simulation of Queues: The R Package queuecomputer (2017) arXiv
- Antony Overstall, David Woods, Maria Adamou: acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange (2017) arXiv
- Jouni Helske, Satu Helske: Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R (2017) arXiv
- Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu: cqrReg: An R Package for Quantile and Composite Quantile Regression and Variable Selection (2017) arXiv
- Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon: Regularized Ordinal Regression and the ordinalNet R Package (2017) arXiv
- Peter E. DeWitt, Samantha MaWhinney, Nichole E. Carlson: cpr: An R Package For Finding Parsimonious B-Spline Regression Models via Control Polygon Reduction and Control Net Reduction (2017) arXiv
- James Balamuta, Roberto Molinari, Stephane Guerrier, Wenchao Yang: The gmwm R package: a comprehensive tool for time series analysis from state-space models to robustness (2016) arXiv
- Nicholas Syring, Meng Li: BayesBD: An R Package for Bayesian Inference on Image Boundaries (2016) arXiv
- Philip Rinn, Pedro G. Lind, Matthias Waechter, Joachim Peinke: The Langevin Approach: An R Package for Modeling Markov Processes (2016) arXiv
- Moores, Matthew T.; Drovandi, Christopher C.; Mengersen, Kerrie; Robert, Christian P.: Pre-processing for approximate Bayesian computation in image analysis (2015)
- Daniel Kosiorowski, Zygmunt Zawadzki: DepthProc An R Package for Robust Exploration of Multidimensional Economic Phenomena (2014) arXiv
- Eddelbuettel, Dirk: Seamless R and C++ integration with Rcpp (2013)
- Raknerud, Arvid; Skare, Ã˜ivind: Indirect inference methods for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes (2012)