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(lme4) gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) gm_all <- allFit(gm) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[lme4]** functions such as `allFit()` and `bootMer()`. The **[lme4]** package fits linear and generalized linear mixed-effects models. Its `allFit()` function fits models using all available optimizers to check for convergence issues, and `bootMer()` performs parametric bootstrap inference. Both are excellent candidates for parallelization. ## Example: Fitting with multiple optimizers The `allFit()` function fits a model with each available optimizer, which can be done in parallel: ```r library(lme4) ## Fit a generalized linear mixed model gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) ## Try all available optimizers gm_all <- allFit(gm) ``` Here `allFit()` evaluates sequentially, but we can easily make it evaluate in parallel by piping to `futurize()`: ```r library(futurize) library(lme4) gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) gm_all <- allFit(gm) |> futurize() ``` This will distribute the optimizer fits 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: Parametric bootstrap The `bootMer()` function performs parametric bootstrap inference on fitted models: ```r library(futurize) plan(multisession) library(lme4) ## Fit a linear mixed model fm <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy) ## Bootstrap the fixed-effect coefficients boot_coef <- function(model) fixef(model) b <- bootMer(fm, boot_coef, nsim = 100) |> futurize() ``` # Supported Functions The following **lme4** functions are supported by `futurize()`: * `allFit()` * `bootMer()` [lme4]: https://cran.r-project.org/package=lme4