NEWS.md

In futurize: Parallelize Common Functions via One Magic Function

Version 0.2.0 (2026-03-17)

Following the initial CRAN release in January 2026, this version adds futurize() support for many more CRAN and Bioconductor packages. To achieve this, support for transpiling S3 and S4 methods was added, expanding beyond regular and generic functions. This opens the door for futurizing many more packages going forward.

Significant Changes

• Add support for futurizing S3 methods where the S3 generic is defined in another package.

• Add support for futurizing S4 methods where the S4 generic is defined in another package.

New Features

• Add support for disabling futurize() transpilation via R option futurize.enable, which may be set via environment variable R_FUTURIZE_ENABLE when the package is loaded.

New Transpilers

• Add support for map-reduce CRAN package pbapply, e.g. y <- pblapply(...) |> futurize().

• Add support for domain-specific Bioconductor package DESeq2, e.g. dds <- DESeq(dds) |> futurize().

• Add support for domain-specific Bioconductor package fgsea, e.g. res <- fgsea(pathways, stats) |> futurize().

• Add support for domain-specific Bioconductor package GenomicAlignments, e.g. se <- summarizeOverlaps(features, bam_files) |> futurize().

• Add support for domain-specific CRAN package fwb, e.g. b <- fwb(data, statistic, R = 1000) |> futurize().

• Add support for domain-specific CRAN package gamlss, e.g. cv <- gamlssCV(y ~ pb(x), data = abdom, K.fold = 10) |> futurize().

• Add support for domain-specific CRAN package glmmTMB, e.g. pr <- profile(m) |> futurize().

• Add support for domain-specific CRAN package kernelshap, e.g. ks <- kernelshap(model, X = x_explain, bg_X = bg_X) |> futurize().

• Add support for domain-specific Bioconductor package GSVA, e.g. es <- gsva(gsvaParam(expr, geneSets)) |> futurize().

• Add support for domain-specific CRAN package metafor, e.g. pr <- profile(fit) |> futurize().

• Add support for domain-specific CRAN package partykit, e.g. cf <- cforest(dist ~ speed, data = cars) |> futurize().

• Add support for domain-specific CRAN package riskRegression, e.g. sc <- Score(list("CSC" = fit), data = d, formula = Hist(time, event) ~ 1, times = 5, B = 100, split.method = "bootcv") |> futurize().

• Add support for domain-specific Bioconductor package scater, e.g. sce <- runPCA(sce) |> futurize().

• Add support for domain-specific Bioconductor package scuttle, e.g. sce <- logNormCounts(sce) |> futurize().

• Add support for Bioconductor package Rsamtools, e.g. counts <- countBam(bamViews) |> futurize().

• Add support for Bioconductor package SingleCellExperiment, e.g. result <- applySCE(sce, perCellQCMetrics) |> futurize().

• Add support for Bioconductor package sva, e.g. adjusted <- ComBat(dat, batch) |> futurize().

• Add support for domain-specific CRAN package seriation, e.g. o <- seriate_best(d_supreme) |> futurize().

• Add support for domain-specific CRAN package SimDesign, e.g. res <- runSimulation(design, replications = 1000, generate, analyse, summarise) |> futurize().

• Add support for domain-specific CRAN package shapr, e.g. result <- explain(model, x_explain, x_train, approach, phi0) |> futurize().

• Add support for domain-specific CRAN package strucchange, e.g. bp <- breakpoints(Nile ~ 1) |> futurize().

• Add support for domain-specific CRAN package TSP, e.g. tour <- solve_TSP(USCA50, method = "nn", rep = 10) |> futurize().

• Add support for domain-specific CRAN package vegan, e.g. md <- mrpp(dune, Management) |> futurize().

Bug Fixes

• futurize() option chunk_size was silently ignored for transpilers relying on doFuture.

• futurize() failed to descend wrapped calls such as local() and suppressWarnings() that specified additional arguments. For example, local({ lapply( ... ) }, envir = env) |> futurize() would produce a parsing error.

• Packages not supporting specifying a random seed will now produce an informative error message if futurize(seed = <numeric>) is specified, e.g. boot, glmmTMB, lme4, mgcv, and vegan.

Version 0.1.0 (2026-01-18)

This is the first version submitted to CRAN.

Version 0.0.6 (2026-01-14)

New Transpilers

• Add support for domain-specific CRAN package mgcv, e.g. b <- bam(...) |> futurize().

New Features

• Add supported_packages() and supported_package_functions().

• Rename argument chunk.size to chunk_size.

• Add custom print() method for transpiled calls such that attributes are displayed for arguments and their content.

• Transpiler can now handle nested, complex wrapped expressions.

• Error messages now suggest using %do% when trying to futurize foreach() with %dopar% or %dofuture%.

• Error messages now distinguish between infix operators (e.g. %do%) and functions (e.g. lapply()).

Version 0.0.5 (2025-11-26)

New Features

• Add support for futurizing calls nested in one or more layers of { ... }, ( ... ), local( ... ), I(), and identity(), e.g. local({ lapply(x, f) }) |> futurize() is the same as local({ lapply(x, f) |> futurize() }).

Version 0.0.4 (2025-09-26)

New Transpilers

• Add support for domain-specific CRAN package tm, e.g. m <- tm_map(crude, content_transformer(tolower)) |> futurize().

New Features

• Handle nested transpilers.

• Add futurize(when = {condition}) for futurizing conditioned on an R expression at runtime, e.g. lapply(xs, fun) |> futurize(when = (length(xs) > 10)).

• Add futurize(FALSE) and futurize(TRUE) for disabling and enabling futurizing of calls.

Version 0.0.3 (2025-08-20)

New Transpilers

• Add support for domain-specific CRAN package caret, e.g. model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize().

• Add support for times() and %:% of foreach, which require special care when it comes to passing future options, e.g. futurize(seed = FALSE).

New Features

• The default future options for futurize() are now customized such that they work in more cases, e.g. there is no need to declare seed = TRUE for replicate(3, rnorm(1)) |> futurize().

• futurize() gained argument eval, which can be used to return the futurized expression instead of evaluating it.

Version 0.0.2 (2025-05-23)

The futurize package unifies our current future.apply, furrr, and doFuture solutions into a minimal, unified API. This means you no longer need to learn those future-specific packages and their APIs, and all you need to know is the ... |> futurize() syntax. The default behavior of futurize() is sufficient for most use cases and users, but, if needed, it comes with one unifying, unique set of arguments that can be used to configure how the futures are resolved, how they are partitioned into chunks, and how output and conditions are relayed, among other things.

New Transpilers

• Add support for base R, e.g. y <- lapply(xs, fcn) |> futurize(), y <- by(xs, idxs, fcn) |> futurize(), and xs <- kernapply(x, k) |> futurize().

• Add support for map-reduce CRAN package purrr, e.g. y <- map(xs, fcn) |> futurize().

• Add support for map-reduce CRAN package crossmap, e.g. y <- xmap_dbl(xs, fcn) |> futurize().

• Add support for map-reduce CRAN package foreach, e.g. y <- foreach(x = xs) %do% { fcn(x) } |> futurize().

• Add support for map-reduce CRAN package plyr, e.g. y <- llply(xs, fcn) |> futurize().

• Add support for map-reduce Bioconductor package BiocParallel, e.g. y <- bplapply(xs, fcn) |> futurize().

• Add support for domain-specific CRAN package boot, e.g. b <- boot(data, statistic, R = 1000) |> futurize().

• Add support for domain-specific CRAN package glmnet, e.g. cv <- cv.glmnet(x, y) |> futurize().

• Add support for domain-specific CRAN package lme4, e.g. gm <- allFit(gm) |> futurize().

Version 0.0.1 (2025-03-07)

New Features

• Implemented a working proof-of-concept of a futurize() function that takes a call expression to any base-R apply function and transpiles it such that it runs in parallel via futures. This works by transpiling the original map-reduce call to evaluate each iteration via a lazy, vanilla future. These futures are then partitioned into chunks, where the number of chunks defaults to the number of parallel workers. The futures in each chunk are merged into a single future. These futures are then launched in parallel on the current future backend. When resolved, the results are reduced back to the structure that the original base R apply function would return.

futurize documentation built on March 19, 2026, 1:07 a.m.