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(parameters) model <- lm(mpg ~ wt, data = mtcars) fit <- bootstrap_model(model, iterations = 1000) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[parameters]** functions, such as `bootstrap_model()` and `bootstrap_parameters()`. The **[parameters]** package (part of the **easystats** ecosystem) provides utilities for processing and summarizing statistical models. The `bootstrap_model()` function generates a distribution of model estimates by refitting the model multiple times using bootstrapped samples. This process can be computationally demanding, especially for complex models or a large number of iterations. Since each bootstrap iteration is independent, it is a perfect candidate for parallelization. ## Example: Bootstrapping a linear model Consider a linear model where we want to obtain bootstrapped estimates of the coefficients: ```r library(parameters) model <- lm(mpg ~ wt + cyl, data = mtcars) ## Generate 1000 bootstrap replicates (sequentially) boot_dist <- bootstrap_model(model, iterations = 1000) ``` To parallelize this using **futurize**, simply pipe the call to `futurize()`: ```r library(futurize) library(parameters) model <- lm(mpg ~ wt + cyl, data = mtcars) ## Generate 1000 bootstrap replicates (in parallel) boot_dist <- bootstrap_model(model, iterations = 1000) |> futurize() ``` This will distribute the bootstrap iterations across the available parallel workers, given that we have set up a parallel backend, e.g. ```r plan(multisession) ``` ## Example: Bootstrapped parameters summary The `bootstrap_parameters()` function is a higher-level wrapper that calls `bootstrap_model()` and then summarizes the results. It can also be parallelized in the same way: ```r library(futurize) plan(multisession) library(parameters) model <- lm(mpg ~ wt + cyl, data = mtcars) boot_params <- bootstrap_parameters(model, iterations = 1000) |> futurize() ``` # Supported Functions The following **parameters** functions are supported by `futurize()`: * `bootstrap_model()` with `seed = TRUE` as the default * `bootstrap_parameters()` with `seed = TRUE` as the default # Without futurize: Manual PSOCK cluster setup For comparison, here is what it takes to parallelize `bootstrap_model()` using the **parallel** package directly, without **futurize**: ```r library(parameters) library(parallel) model <- lm(mpg ~ wt + cyl, data = mtcars) ## Set up a PSOCK cluster ncpus <- 4L cl <- makeCluster(ncpus) ## Run bootstrapping in parallel boot_dist <- bootstrap_model(model, iterations = 1000, parallel = "snow", n_cpus = ncpus, cluster = cl) ## Tear down the cluster stopCluster(cl) ``` With **futurize**, the cluster management is handled automatically. You just control the backend with `plan()`. [parameters]: https://cran.r-project.org/package=parameters [other parallel backends]: https://www.futureverse.org/backends.html