foreach

foreach: Foreach looping construct for R. Support for the foreach looping construct. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn’t require the evaluation of a function. Using foreach without side effects also facilitates executing the loop in parallel.

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References in zbMATH (referenced in 20 articles )

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  1. Garth Tarr; Samuel Müller; Alan Welsh: mplot: An R Package for Graphical Model Stability and Variable Selection Procedures (2018)
  2. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018)
  3. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  4. Salim Khalil, Mohamed Fakir: RCrawler: An R package for parallel web crawling and scraping (2017)
  5. Aaron King; Dao Nguyen; Edward Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2016)
  6. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016)
  7. Marius Hofert; Martin Mächler: Parallel and Other Simulations in R Made Easy: An End-to-End Study (2016)
  8. Panagiotis Papastamoulis, Magnus Rattray: BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data (2016) arXiv
  9. Ricardo Oliveros-Ramos, Yunne-Jai Shin: Calibrar: an R package for fitting complex ecological models (2016) arXiv
  10. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the R package regRSM (2016)
  11. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  12. Aaron A. King, Dao Nguyen, Edward L. Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2015) arXiv
  13. Bernd Bischl; Michel Lang; Olaf Mersmann; Jörg Rahnenführer; Claus Weihs: BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments (2015)
  14. Geir Berentsen; Tore Kleppe; Dag Tjøstheim: Introducing localgauss, an R Package for Estimating and Visualizing Local Gaussian Correlation (2014)
  15. Lawrence, Michael; Morgan, Martin: Scalable genomics with R and bioconductor (2014)
  16. Mahmoudian, Behzad; Mohammadzadeh, Mohsen: A spatio-temporal dynamic regression model for extreme wind speeds (2014)
  17. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  18. Holst, Klaus Kähler; Budtz-Jørgensen, Esben: Linear latent variable models: the lava-package (2013)
  19. Michael Kane; John Emerson; Stephen Weston: Scalable Strategies for Computing with Massive Data (2013)
  20. Hadley Wickham: The Split-Apply-Combine Strategy for Data Analysis (2011)