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The futurize package allows you to easily turn sequential code
into parallel code by piping the sequential code to the futurize()
function. Easy!
library(futurize)
plan(multisession)
library(strucchange)
data("Nile")
bp.nile <- breakpoints(Nile ~ 1) |> futurize()
The strucchange package provides the breakpoints() function
for estimating one or more change points in a data trace,
e.g. in time-series data.
library(futurize)
plan(multisession)
library(strucchange)
## UK Seatbelt data: a SARIMA(1,0,0)(1,0,0)_12 model
## (fitted by OLS) is used and reveals (at least) two
## breakpoints - one in 1973 associated with the oil crisis and
## one in 1983 due to the introduction of compulsory
## wearing of seatbelts in the UK.
data("UKDriverDeaths")
seatbelt <- log10(UKDriverDeaths)
seatbelt <- cbind(seatbelt, lag(seatbelt, k = -1), lag(seatbelt, k = -12))
colnames(seatbelt) <- c("y", "ylag1", "ylag12")
seatbelt <- window(seatbelt, start = c(1970, 1), end = c(1984, 12))
plot(seatbelt[,"y"], ylab = expression(log[10](casualties)))
## testing
re.seat <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "RE")
plot(re.seat)
## dating
bp.seat <- breakpoints(y ~ ylag1 + ylag12, data = seatbelt, h = 0.1) |> futurize()
lines(bp.seat, breaks = 2)
This will parallelize the dynamic programming algorithm for computing the optimal breakpoints, given that we have set up parallel workers, e.g.
plan(multisession)
The built-in multisession backend parallelizes on your local
computer and works on all operating systems. There are other
parallel backends to choose from, including alternatives to
parallelize locally as well as distributed across remote machines,
e.g.
plan(future.mirai::mirai_multisession)
and
plan(future.batchtools::batchtools_slurm)
The following strucchange functions are supported by futurize():
breakpoints() for 'formula'For comparison, here is what it takes to parallelize
breakpoints() using the parallel and doParallel packages
directly, without futurize:
library(strucchange)
library(parallel)
library(doParallel)
data("Nile")
## Set up a PSOCK cluster and register it with foreach
ncpus <- 4L
cl <- makeCluster(ncpus)
registerDoParallel(cl)
## Find breakpoints in parallel via foreach
bp.nile <- breakpoints(Nile ~ 1, hpc = "foreach")
## Tear down the cluster
stopCluster(cl)
registerDoSEQ() ## reset foreach to sequential
This requires you to manually create a cluster, register it with
doParallel, and remember to tear it down and reset the
foreach backend when done. If you forget to call
stopCluster(), or if your code errors out before reaching it, you
leak background R processes. You also have to decide upfront how
many CPUs to use and what cluster type to use. Switching to another
parallel backend, e.g. a Slurm cluster, would require a completely
different setup. With futurize, all of this is handled for you - just pipe
to futurize() and control the backend with plan().