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 53 articles )

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  1. Beyaztas, Ufuk; Shang, Han Lin: Function-on-function linear quantile regression (2022)
  2. Cameron, James; Bagchi, Pramita: A test for heteroscedasticity in functional linear models (2022)
  3. Arnald Puy, Samuele Lo Piano, Andrea Saltelli, Simon A. Levin: sensobol: an R package to compute variance-based sensitivity indices (2021) arXiv
  4. Christian Thiele; Gerrit Hirschfeld: cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R (2021) not zbMATH
  5. David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
  6. Eggleston, B. S., Ibrahim, J. G., McNeil, B., Catellier, D: BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data (2021) not zbMATH
  7. Johnson, Devin; Pelland, Noel; Sterling, Jeremy: A continuous-time semi-Markov model for animal movement in a dynamic environment (2021)
  8. Paynter, Alex; Willis, Amy D.: Tuning parameter selection for a penalized estimator of species richness (2021)
  9. Samuel Borms, David Ardia, Keven Bluteau, Kris Boudt, Jeroen Van Pelt, Andres Algaba: The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment (2021) not zbMATH
  10. Tyler Morgan-Wall, George Khoury: Optimal Design Generation and Power Evaluation in R: The skpr Package (2021) not zbMATH
  11. Vinue, Guillermo; Epifanio, Irene: Robust archetypoids for anomaly detection in big functional data (2021)
  12. Begüm D. Topçuoğlu; Zena Lapp; Kelly L. Sovacool; Evan Snitkin; Jenna Wiens; Patrick D. Schloss: mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines (2020) not zbMATH
  13. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  14. 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
  15. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  16. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
  17. Tickle, S. O.; Eckley, I. A.; Fearnhead, P.; Haynes, K.: Parallelization of a common changepoint detection method (2020)
  18. Touloupou, Panayiota; Finkenstädt, Bärbel; Spencer, Simon E. F.: Scalable Bayesian inference for coupled hidden Markov and semi-Markov models (2020)
  19. Wickramasuriya, Shanika L.; Turlach, Berwin A.; Hyndman, Rob J.: Optimal non-negative forecast reconciliation (2020)
  20. Xu Dong, Luis Castro, Nazrul Shaikh: fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks (2020) not zbMATH

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