# Parallelize 'modelsummary' 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(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,

    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.

    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 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