---
title: "useR! 2026: Futurize – Tearing Down Parallelization Barriers in R with Transpilers"
url: https://daily.dev/posts/user-2026-futurize-tearing-down-parallelization-barriers-in-r-with-transpilers-gtapwq8y8
source_url: https://www.r-bloggers.com/2026/08/user-2026-futurize-tearing-down-parallelization-barriers-in-r-with-transpilers
type: article
source: "R-bloggers"
published: 2026-08-20T15:09:29.413Z
updated: 2026-08-20T15:09:52.347Z
tags: ["data-science", "r"]
reading_time: 2
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# useR! 2026: Futurize – Tearing Down Parallelization Barriers in R with Transpilers

**[R-bloggers](https://daily.dev/sources/rbloggers)** · 2 min read · 0 upvotes · 0 comments

## Summary

Henrik Bengtsson presented a talk at the useR! 2026 conference in Warsaw, Poland, introducing the new futurize package, part of the futureverse ecosystem. The package lets R users parallelize existing map-reduce style calls (lapply, purrr::map, foreach, plyr::llply) simply by piping them to futurize(), and it also supports domain-specific functions like stats::kernapply, boot, caret::train, glmnet::cv.glmnet, and lme4::allFit. Slides (16 slides, 18 minutes) are linked, with video to appear. The post also notes next year's useR! conference will be held in Santiago, Chile in July 2027.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.r-bloggers.com/2026/08/user-2026-futurize-tearing-down-parallelization-barriers-in-r-with-transpilers>

## Questions this post answers

### How do I parallelize an lapply call in R without rewriting it as a future_lapply call?

Pipe the existing call to the futurize() function from the new futurize package. For example, ys <- lapply(xs, fit_model) |> futurize() parallelizes the call automatically. The same pattern works for purrr::map, foreach %do%, and plyr::llply calls, requiring no rewrite of the underlying logic.

_Developers weighing parallelization approaches in R can follow futureverse updates like this on daily.dev._

### Which R modeling functions can be parallelized with the futurize package?

The futurize package supports domain-specific calls beyond basic map-reduce functions, including stats::kernapply, boot::boot, caret::train, glmnet::cv.glmnet, and lme4::allFit. Each can be parallelized simply by piping the call to futurize(), for example model <- caret::train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize().

_Anyone speeding up R model training workflows can track futurize package updates on daily.dev._

## Similar posts on daily.dev

- [A world still to be mapped: reflections on geocomputation in R: takeaways from the talk and workshop at UseR\! 2026](https://daily.dev/posts/a-world-still-to-be-mapped-reflections-on-geocomputation-in-r-takeaways-from-the-talk-and-workshop-xsebo6zbi) · R-bloggers · 0 upvotes · 0 comments
- [LatinR 2025: future.p2p: Peer-to-Peer Compute Clusters in R](https://daily.dev/posts/latinr-2025-future-p2p-peer-to-peer-compute-clusters-in-r-lpg9rgqjs) · R-bloggers · 0 upvotes · 0 comments
- [Graceful Internet Packages](https://daily.dev/posts/graceful-internet-packages-3zmkz0y4y) · R-bloggers · 0 upvotes · 0 comments

---

Tags: [#data-science](https://daily.dev/tags/data-science), [#r](https://daily.dev/tags/r)

[View this post on daily.dev](https://daily.dev/posts/user-2026-futurize-tearing-down-parallelization-barriers-in-r-with-transpilers-gtapwq8y8)