The **futurize** package allows you to easily turn sequential code into parallel code by piping the sequential code to the `futurize()` function. Easy! # TL;DR ```r library(futurize) plan(multisession) library(sandwich) fm <- lm(dist ~ speed, data = cars) v <- vcovBS(fm) |> futurize() ``` # Introduction The **[sandwich]** package provides model-agnostic robust covariance matrix estimators. ## Example: Clustered bootstrap covariance matrix Example adopted from `help("vcovBS", package = "sandwich")`: ```r library(futurize) plan(multisession) library(sandwich) ## fit a simple linear model fm <- lm(dist ~ speed, data = cars) ## bootstrap covariance matrix estimation in parallel v <- vcovBS(fm, R = 250) |> futurize() ## summary of coefficients with robust standard errors library(lmtest) coeftest(fm, vcov = v) ``` This will parallelize the bootstrap replications, given that we have set up parallel workers, e.g. ```r 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. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` # Supported Functions The following **sandwich** functions are supported by `futurize()`: * `vcovBS()` with `seed = TRUE` as the default * `vcovJK()` with `seed = TRUE` as the default # Without futurize: Manual setup For comparison, here is what it takes to parallelize `vcovBS()` using the **sandwich** package directly, without **futurize**: ```r library(sandwich) library(parallel) ## Fit a simple linear model fm <- lm(dist ~ speed, data = cars) ## Bootstrap covariance matrix estimation in parallel using cores v <- vcovBS(fm, R = 250, cores = 4L) ``` While **sandwich** has a built-in `cores` argument, it only supports local multicore or PSOCK clusters depending on the OS. With **futurize**, you can use any `future` backend, including remote clusters and HPC environments, just by piping to `futurize()` and controlling the backend with `plan()`. [sandwich]: https://cran.r-project.org/package=sandwich [other parallel backends]: https://www.futureverse.org/backends.html