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.


References in zbMATH (referenced in 33 articles )

Showing results 1 to 20 of 33.
Sorted by year (citations)

1 2 next

  1. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  2. Fernando S. Marques, José H. H. Grisi-Filho, Jean C. R. Silva, Erivânia C. Almeida, José L. Silva Júnior: hybridModels: An R Package for the Stochastic Simulation of Disease Spreading in Dynamic Networks (2020) not zbMATH
  3. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  4. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
  5. Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
  6. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  7. Górecki, Tomasz; Smaga, Łukasz: fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data (2019)
  8. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  9. Liang, Waley W. J.; Lee, Herbert K. H.: Bayesian nonstationary Gaussian process models via treed process convolutions (2019)
  10. Robert Geitner and Robby Fritzsch and Jürgen Popp and Thomas Bocklitz: corr2D: Implementation of Two-Dimensional Correlation Analysis in R (2019) not zbMATH
  11. Ziwen An, Leah F. South, Christopher C. Drovand: BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood (2019) arXiv
  12. Garth Tarr; Samuel Müller; Alan Welsh: mplot: An R Package for Graphical Model Stability and Variable Selection Procedures (2018) not zbMATH
  13. Guo, Jia; Zhou, Bu; Zhang, Jin-Ting: Testing the equality of several covariance functions for functional data: a supremum-norm based test (2018)
  14. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018) not zbMATH
  15. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  16. Salim Khalil, Mohamed Fakir: RCrawler: An R package for parallel web crawling and scraping (2017) not zbMATH
  17. Aaron King; Dao Nguyen; Edward Ionides: Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2016) not zbMATH
  18. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016) not zbMATH
  19. Liquet, Benoit; Saracco, Jerome: BIG-SIR: a sliced inverse regression approach for massive data (2016)
  20. Marius Hofert; Martin Mächler: Parallel and Other Simulations in R Made Easy: An End-to-End Study (2016) not zbMATH

1 2 next