# Parallelize 'gamlss' 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(gamlss)

    data(abdom, package = "gamlss.data")
    cv <- gamlssCV(y ~ pb(x), data = abdom, K.fold = 10) |> futurize()

# Introduction

This vignette demonstrates how to use this approach to parallelize gamlss functions such as `gamlssCV()`, `add1All()`, `drop1All()`, `add1TGD()`, and `drop1TGD()`.

The gamlss package implements Generalized Additive Models for Location, Scale, and Shape (GAMLSS). GAMLSS models extend traditional generalized additive models (GAMs) by allowing all parameters of a distribution — not just the mean — to be modeled as functions of explanatory variables. The package provides tools for model fitting, selection, and diagnostics.

Several gamlss functions support parallel evaluation via the `parallel`, `ncpus`, and `cl` arguments. By piping to `futurize()`, you can leverage any future-based parallel backend for these computations.

## Example: k-fold cross-validation

The `gamlssCV()` function performs k-fold cross-validation for model selection. This is computationally intensive and benefits greatly from parallelization:

    library(gamlss)

    data(abdom, package = "gamlss.data")
    cv <- gamlssCV(y ~ pb(x), data = abdom, K.fold = 10)

Here `gamlssCV()` evaluates each fold sequentially, but we can easily make it evaluate in parallel by piping to `futurize()`:

    library(futurize)
    library(gamlss)

    data(abdom, package = "gamlss.data")
    cv <- gamlssCV(y ~ pb(x), data = abdom, K.fold = 10) |> futurize()

This will distribute the cross-validation folds across the available parallel workers, given that we have set up parallel workers, e.g.

    plan(multisession)

Unlike other parallel backends in R, `futurize()` relays standard output, messages, and warnings produced by the parallel workers back to your main R session. For instance, when running the above, you will see the progress output from each optimizer as it completes:

    > cv <- gamlssCV(y ~ pb(x), data = abdom, K.fold = 10) |> futurize()
    fold 1
    fold 2
    fold 3
    fold 4
    fold 5
    fold 6
    fold 7
    fold 8
    fold 9
    fold 10
    >

This output originates from the parallel workers and is relayed to your R session, so you get the same informative feedback as when running sequentially.

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)

## Example: Drop terms from a model

The `drop1All()` function evaluates the effect of dropping each term from a fitted GAMLSS model, which can be parallelized:

    library(futurize)
    plan(multisession)
    library(gamlss)

    data(abdom, package = "gamlss.data")
    m <- gamlss(y ~ pb(x) + x, data = abdom)
    d <- drop1All(m) |> futurize()

# Supported Functions

The following gamlss functions are supported by `futurize()`:

- `add1All()`
- `add1TGD()`
- `drop1All()`
- `drop1TGD()`
- `gamlssCV()` with `seed = TRUE` as the default

# Without futurize: Manual PSOCK cluster setup

For comparison, here is what it takes to parallelize `gamlssCV()` using the parallel package directly, without futurize:

    library(gamlss)
    library(parallel)

    data(abdom, package = "gamlss.data")

    ## Set up a PSOCK cluster
    ncpus <- 4L
    cl <- makeCluster(ncpus)
    clusterEvalQ(cl, library(gamlss))

    ## Perform k-fold cross-validation in parallel
    cv <- gamlssCV(y ~ pb(x), data = abdom, K.fold = 10,
                   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()`.