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(fwb) set.seed(123) lm_fit <- lm(mpg ~ wt + am, data = mtcars) b <- fwb(mtcars, statistic = function(data, w) { fit <- lm(mpg ~ wt + am, data = data, weights = w) coef(fit) }, R = 999) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[fwb]** functions such as `fwb()` and `vcovFWB()`. The **[fwb]** package implements the fractional weighted bootstrap (also known as the Bayesian bootstrap). Rather than resampling units to include in bootstrap samples, random weights are drawn and applied to a weighted estimator. Given the resampling nature of bootstrapping, the algorithm is an excellent candidate for parallelization. ## Example: Fractional weighted bootstrap The `fwb()` function produces fractional weighted bootstrap samples of a statistic applied to data. For example, consider bootstrapping a linear model on the `mtcars` dataset: ```r library(fwb) ## Draw 999 bootstrap samples of the regression coefficients set.seed(123) b <- fwb(mtcars, statistic = function(data, w) { fit <- lm(mpg ~ wt + am, data = data, weights = w) coef(fit) }, R = 999) ``` Here `fwb()` evaluates sequentially, but we can easily make it evaluate in parallel by piping to `futurize()`: ```r library(fwb) library(futurize) set.seed(123) b <- fwb(mtcars, statistic = function(data, w) { fit <- lm(mpg ~ wt + am, data = data, weights = w) coef(fit) }, R = 999) |> futurize() ``` This will distribute the 999 bootstrap samples across the available parallel workers, 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) ``` ## Example: Bootstrap variance-covariance matrix The `vcovFWB()` function computes a bootstrap variance-covariance matrix for model coefficients: ```r library(futurize) plan(multisession) library(fwb) lm_fit <- lm(mpg ~ wt + am, data = mtcars) V <- vcovFWB(lm_fit, R = 999) |> futurize() ``` # Supported Functions The following **fwb** functions are supported by `futurize()`: * `fwb()` with `seed = TRUE` as the default * `vcovFWB()` with `seed = TRUE` as the default [fwb]: https://cran.r-project.org/package=fwb [other parallel backends]: https://www.futureverse.org/backends.html