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(glmmTMB) m <- glmmTMB(count ~ mined + (1 | site), data = Salamanders, family = nbinom2) pr <- profile(m) |> futurize() ``` # Introduction This vignette demonstrates how to parallelize **[glmmTMB]** functions such as `profile()` through `futurize()`. The **[glmmTMB]** package fits generalized linear mixed models (GLMMs) using Template Model Builder (TMB). Its `profile()` function computes likelihood profiles for model parameters. These computations are performed independently for each parameter, making them candidates for parallelization. ## Example: Likelihood profile The `profile()` function computes the likelihood profile for each model parameter. For example, using the built-in `Salamanders` dataset to model salamander counts: ```r library(glmmTMB) ## Fit a negative binomial GLMM m <- glmmTMB(count ~ mined + (1 | site), data = Salamanders, family = nbinom2) ## Compute likelihood profile pr <- profile(m) ``` Here `profile()` is calculated sequentially. To calculate in parallel, we can pipe to `futurize()`: ```r library(futurize) library(glmmTMB) m <- glmmTMB(count ~ mined + (1 | site), data = Salamanders, family = nbinom2) pr <- profile(m) |> futurize() ``` This will distribute the per-parameter profile computations 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) ``` # Supported Functions The following **glmmTMB** functions are supported by `futurize()`: * `profile()` for 'glmmTMB' # Without futurize: Manual PSOCK cluster setup For comparison, here is what it takes to parallelize `profile()` using the **parallel** package directly, without **futurize**: ```r library(glmmTMB) library(parallel) ## Fit a negative binomial GLMM m <- glmmTMB(count ~ mined + (1 | site), data = Salamanders, family = nbinom2) ## Set up a PSOCK cluster ncpus <- 4L cl <- makeCluster(ncpus) ## Compute likelihood profile in parallel pr <- profile(m, parallel = "snow", ncpus = ncpus, cl = cl) ## Tear down the cluster stopCluster(cl) ``` This requires you to manually create and manage the cluster lifecycle. If you forget to call `stopCluster()`, or if your code errors out before reaching it, you leak background R processes. You also have to decide upfront how many CPUs to use and what cluster type to use. Switching to another parallel backend, e.g. a Slurm cluster, would require a completely different setup. With **futurize**, all of this is handled for you - just pipe to `futurize()` and control the backend with `plan()`. [glmmTMB]: https://cran.r-project.org/package=glmmTMB [other parallel backends]: https://www.futureverse.org/backends.html