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Add support for descending calls wrapped in
with().
Add default future labels that reflect what function call has been futurized.
Errors produced when an unsupported call is attempted now includes the version of the futurize package.
futurize(label = name) was ignored for
crossmap and purrr calls.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.
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.
futurize() transpilation via
R option futurize.enable, which may be set via environment
variable R_FUTURIZE_ENABLE when the package is loaded.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().
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.
This is the first version submitted to CRAN.
b <- bam(...) |> futurize().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()).
{ ... }, ( ... ), local( ... ),
I(), and identity(), e.g.
local({ lapply(x, f) }) |> futurize() is the same as
local({ lapply(x, f) |> futurize() }).m <- tm_map(crude, content_transformer(tolower)) |> futurize().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.
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).
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.
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.
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().
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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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