A Unifying Framework for Parallel and Distributed Processing in R using Futures
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Abstract (arXiv)
A future is a programming construct designed for concurrent and asynchronous evaluation of code, making it particularly useful for parallel processing. The future package implements the Future API for programming with futures in R. This minimal API provides sufficient constructs for implementing parallel versions of well-established, high-level map-reduce APIs. The future ecosystem supports exception handling, output and condition relaying, parallel random number generation, and automatic identification of globals lowering the threshold to parallelize code. The Future API bridges parallel frontends with parallel backends following the philosophy that end-users are the ones who choose the parallel backend while the developer focuses on what to parallelize. A variety of backends exist and third-party contributions meeting the specifications, which ensure that the same code works on all backends, are automatically supported. The future framework solves several problems not addressed by other parallel frameworks in R.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2020.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 2; 2025: 2; 2026: 2.
9 papers.
- 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
- 2025shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Pythoncited by 5
- 2021Mutation is all you needcited by 3
- 2021A framework for causal segmentation analysis with machine learning in large-scale digital experimentscited by 1
- 2024mlr3summary: Concise and interpretable summaries for machine learning modelscited by 0
- 2024Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learningcited by 0
- 2025flowengineR: A Modular and Extensible Framework for Fair and Reproducible Workflow Design in Rcited by 0
- 2026mlr3mbo: Bayesian Optimization in Rcited by 0
- 2026Topological summaries of fingerprint ridge patterns carry identity informationcited by 0
- 2026Topological summaries of fingerprint ridge patterns carry identity information20 Jun 2026
- 2026mlr3mbo: Bayesian Optimization in R31 Mar 2026
- 2025flowengineR: A Modular and Extensible Framework for Fair and Reproducible Workflow Design in R29 Oct 2025
- 2025shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python2 Apr 2025
- 2024Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning20 Sep 2024
- 2024mlr3summary: Concise and interpretable summaries for machine learning models25 Apr 2024
- 2021A framework for causal segmentation analysis with machine learning in large-scale digital experiments1 Nov 2021
- 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges13 Jul 2021
- 2021Mutation is all you need4 Jul 2021