# Parallelize 'sandwich' functions

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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!

# TL;DR

    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")`:

    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.

    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)

# 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:

    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()`.