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(modelsummary) fit1 <- lm(mpg ~ cyl, data = mtcars) fit2 <- lm(mpg ~ cyl + hp, data = mtcars) models <- list(Model1 = fit1, Model2 = fit2) modelsummary(models) |> futurize() ``` # Introduction The **[modelsummary]** package creates customizable tables and plots to summarize statistical models side-by-side. For example, ```r library(futurize) plan(multisession) library(modelsummary) ## fit multiple linear models fit1 <- lm(mpg ~ cyl, data = mtcars) fit2 <- lm(mpg ~ cyl + hp, data = mtcars) models <- list(Model1 = fit1, Model2 = fit2) ## generate modelsummary table in parallel tbl <- modelsummary(models, output = "data.frame") |> futurize() print(tbl) ``` will parallelize model summary statistics extraction, 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 **modelsummary** functions are supported by `futurize()`: * `modelsummary()` with `seed = TRUE` as the default * `msummary()` with `seed = TRUE` as the default * `modelplot()` with `seed = TRUE` as the default [modelsummary]: https://cran.r-project.org/package=modelsummary [other parallel backends]: https://www.futureverse.org/backends.html