R package future. A Future API for R is provided. In programming, a future is an abstraction for a value that may be available at some point in the future. The state of a future can either be unresolved or resolved. As soon as it is resolved, the value is available. Futures are useful constructs in for instance concurrent evaluation, e.g. parallel processing and distributed processing on compute clusters. The purpose of this package is to provide a lightweight interface for using futures in R. Functions ’future()’ and ’value()’ exist for creating futures and requesting their values, e.g. ’f <- future({ mandelbrot(-0.75, 0, side=3) })’ and ’v <- value(f)’. The ’resolved()’ function can be used to check if a future is resolved or not. An infix assignment operator ’%<-%’ exists for creating futures whose values are accessible by the assigned variables (as promises), e.g. ’v %<-% { mandelbrot(-0.75, 0, side=3) }’. This package implements synchronous ”lazy” and ”eager” futures, and asynchronous ”multicore”, ”multisession” and ad hoc ”cluster” futures. Globals variables and functions are automatically identified and exported. Required packages are attached in external R sessions whenever needed. All types of futures are designed to behave the same such that the exact same code work regardless of futures used or number of cores, background sessions or cluster nodes available. Additional types of futures are provided by other packages enhancing this package.

References in zbMATH (referenced in 10 articles )

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  1. Etienne Côme, Nicolas Jouvin : greed: An R Package for Model-Based Clustering by Greedy Maximization of the Integrated Classification Likelihood (2022) arXiv
  2. Jeffrey W. Hollister, Dorothy Q. Kellogg, Qian Lei-Parent, Emily Wilson, Cary Chadwick, David Dickson, Arthur Gold, Chester Arnold: nsink: An R package for flow path nitrogen removal estimation (2022) not zbMATH
  3. Erler, N. S., Rizopoulos, D., Lesaffre, E. M. E. H.: JointAI: Joint Analysis and Imputation of Incomplete Data in R (2021) not zbMATH
  4. Johnson, Devin; Pelland, Noel; Sterling, Jeremy: A continuous-time semi-Markov model for animal movement in a dynamic environment (2021)
  5. Maximillian H.K. Hesselbarth: shar: An R package to analyze species-habitat associations using point pattern analysis (2021) not zbMATH
  6. Philippe Boileau, Nima Hejazi, Brian Collica, Jamarcus Liu, Mark van der Laan, Sandrine Dudoit : cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R (2021) not zbMATH
  7. Tyler Morgan-Wall, George Khoury: Optimal Design Generation and Power Evaluation in R: The skpr Package (2021) not zbMATH
  8. Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
  9. 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
  10. SimonBehrendt; ThomasDimpfl; Franziska J.Peter; David J.Zimmermann: RTransferEntropy - Quantifying information flow between different time series using effective transfer entropy (2019) not zbMATH