# Step by step guide for creating a package that depends on RStan

#### Stefan Siegert, Jonah Gabry, Martin Lysy, and Ben Goodrich

#### 2024-01-22

## Introduction

In this vignette we will walk through the steps necessary for creating an R package that depends on Stan by creating a package with one function that fits a simple linear regression. Before continuing, we recommend that you first read the other vignette Guidelines for Developers of R Packages Interfacing with Stan.

## Creating the package skeleton

The rstantools package offers two methods for adding Stan functionality to R packages:

- `rstan_create_package()`: set up a new R package with Stan programs
- `use_rstan()`: add Stan functionality to an existing R package

Here we will use `rstan_create_package()` to initialize a bare-bones package directory. The name of our demo package will be rstanlm; it will fit a simple linear regression model using Stan.

    library("rstantools")
    rstan_create_package(path = 'rstanlm')

    This is rstantools version 2.4.0

    Creating package skeleton for package: rstanlm

    Package: rstanlm
    Title: What the Package Does (One Line, Title Case)
    Version: 0.0.0.9000
    Authors@R (parsed):
        * First Last <first.last@example.com> [aut, cre] (YOUR-ORCID-ID)
    Description: What the package does (one paragraph).
    License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
        license
    Encoding: UTF-8
    Roxygen: list(markdown = TRUE)
    RoxygenNote: 7.3.0

    Creating inst/stan/include directory ...

    Creating inst/include directory ...

    Creating src directory ...

    Updating DESCRIPTION ...

    Adding 'configure' files ...

    Next, add the following lines (e.g., via <package-name>-package.R if using roxygen) to your NAMESPACE:

    import(Rcpp)
    import(methods)
    importFrom(rstan, sampling)
    importFrom(rstantools, rstan_config)
    importFrom(RcppParallel, RcppParallelLibs)
    useDynLib(rstanlm, .registration = TRUE)

    Done.

    Adding rstanlm-package.R file ...

    Adding .gitignore file ...

    Adding .Rbuildignore file ...

    Configuring Stan compile and module export instructions ...

    Further Stan-specific steps are described in 'rstanlm/Read-and-delete-me'.

If we had existing `.stan` files to include with the package we could use the optional `stan_files` argument to `rstan_create_package()` to include them. Another option, which we’ll use below, is to add the Stan files once the basic structure of the package is in place.

We can now set the new working directory to the new package directory and view the contents. (Note: if using RStudio then by default the newly created project for the package will be opened in a new session and you will not need the call to `setwd()`.)

    setwd("rstanlm")
    list.files(all.files = TRUE)

     [1] "."                  ".."                 ".Rbuildignore"
     [4] ".gitignore"         "DESCRIPTION"        "NAMESPACE"
     [7] "R"                  "Read-and-delete-me" "configure"
    [10] "configure.win"      "inst"               "src"

    file.show("DESCRIPTION")

    Package: rstanlm
    Title: What the Package Does (One Line, Title Case)
    Version: 0.0.0.9000
    Authors@R:
        person("First", "Last", , "first.last@example.com", role = c("aut", "cre"),
               comment = c(ORCID = "YOUR-ORCID-ID"))
    Description: What the package does (one paragraph).
    License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
        license
    Encoding: UTF-8
    Roxygen: list(markdown = TRUE)
    RoxygenNote: 7.3.0
    Biarch: true
    Depends:
        R (>= 3.4.0)
    Imports:
        Rcpp (>= 0.12.0),
        RcppParallel (>= 5.0.1),
        methods,
        rstan (>= 2.18.1),
        rstantools (>= 2.4.0)
    LinkingTo:
        BH (>= 1.66.0),
        Rcpp (>= 0.12.0),
        RcppEigen (>= 0.3.3.3.0),
        RcppParallel (>= 5.0.1),
        StanHeaders (>= 2.18.0),
        rstan (>= 2.18.1)
    SystemRequirements: GNU make

Some of the sections in the `DESCRIPTION` file need to be edited by hand (e.g., `Title`, `Author`, `Maintainer`, and `Description`, but these also can be set with the `fields` argument to `rstan_create_package()`). However, `rstan_create_package()` has added the necessary packages and versions to `Depends`, `Imports`, and `LinkingTo` to enable Stan functionality.

## Read-and-delete-me file

Before deleting the `Read-and-delete-me` file in the new package directory make sure to read it because it contains some important instructions about customizing your package:

    file.show("Read-and-delete-me")

    Stan-specific notes:

    * All '.stan' files containing stanmodel definitions must be placed in 'inst/stan'.
    * Additional files to be included by stanmodel definition files
      (via e.g., #include "mylib.stan") must be placed in any subfolder of 'inst/stan'.
    * Additional C++ files needed by any '.stan' file must be placed in 'inst/include',
      and can only interact with the Stan C++ library via '#include' directives
      placed in the file 'inst/include/stan_meta_header.hpp'.
    * The precompiled stanmodel objects will appear in a named list called 'stanmodels',
      and you can call them with e.g., 'rstan::sampling(stanmodels$foo, ...)'

You can move this file out of the directory, delete it, or list it in the `.Rbuildignore` file if you want to keep it in the directory.

    file.remove('Read-and-delete-me')

    [1] TRUE

## Stan files

Our package will call rstan’s `sampling()` method to use MCMC to fit a simple linear regression model for an outcome variable `y` with a single predictor `x`. After writing the necessary Stan program, the file should be saved with a `.stan` extension in the `inst/stan` subdirectory. We’ll save the following program to `inst/stan/lm.stan`:

    // Save this file as inst/stan/lm.stan
    data {
      int<lower=1> N;
      vector[N] x;
      vector[N] y;
    }
    parameters {
      real intercept;
      real beta;
      real<lower=0> sigma;
    }
    model {
      // ... priors, etc.

      y ~ normal(intercept + beta * x, sigma);
    }

The `inst/stan` subdirectory can contain additional Stan programs if required by your package. During installation, all Stan programs will be compiled and saved in the list `stanmodels` that can then be used by R function in the package. The rule is that the Stan program compiled from the model code in `inst/stan/foo.stan` is stored as list element `stanmodels$foo`. Thus, the filename of the Stan program in the `inst/stan` directory should not contain spaces or dashes and nor should it start with a number or utilize non-ASCII characters.

## R files

We next create the file `R/lm_stan.R` where we define the function `lm_stan()` in which our compiled Stan model is being used. Setting the `rstan_create_package()` argument `roxygen = TRUE` (the default value) enables roxygen2 documentation for the package functions. The following comment block placed in `lm_stan.R` ensures that the function has a help file and that it is added to the package `NAMESPACE`:

    # Save this file as `R/lm_stan.R`

    #' Bayesian linear regression with Stan
    #'
    #' @export
    #' @param x Numeric vector of input values.
    #' @param y Numeric vector of output values.
    #' @param ... Arguments passed to `rstan::sampling` (e.g. iter, chains).
    #' @return An object of class `stanfit` returned by `rstan::sampling`
    #'
    lm_stan <- function(x, y, ...) {
      standata <- list(x = x, y = y, N = length(y))
      out <- rstan::sampling(stanmodels$lm, data = standata, ...)
      return(out)
    }

When roxygen2 documentation is enabled, a top-level package file `R/rstanlm-package.R` is created by `rstan_create_package()` to import necessary functions for other packages and to set up the package for compiling Stan C++ code:

    file.show(file.path("R", "rstanlm-package.R"))

    #' The 'rstanlm' package.
    #'
    #' @description A DESCRIPTION OF THE PACKAGE
    #'
    #' @docType package
    #' @name rstanlm-package
    #' @aliases rstanlm
    #' @useDynLib rstanlm, .registration = TRUE
    #' @import methods
    #' @import Rcpp
    #' @importFrom rstan sampling
    #' @importFrom rstantools rstan_config
    #' @importFrom RcppParallel RcppParallelLibs
    #'
    #' @references
    #' Stan Development Team (NA). RStan: the R interface to Stan. R package version 2.32.3. https://mc-stan.org
    #'
    NULL

The `#' @description` section can be manually edited to provided specific information about the package.

## Documentation

With roxygen documentation enabled, we need to generate the documentation for `lm_stan` and update the `NAMESPACE` so the function is exported, i.e., available to users when the package is installed. This can be done with the function `roxygen2::roxygenize()`, which needs to be called twice initially.

    try(roxygen2::roxygenize(load_code = rstantools_load_code), silent = TRUE)
    roxygen2::roxygenize()

    Writing 'NAMESPACE'
    ℹ Loading rstanlm
    ℹ Re-compiling rstanlm (debug build)

    ✖ rstanlm-package.R:18: `@docType "package"` is deprecated.
    ℹ Please document "_PACKAGE" instead.
    Writing 'NAMESPACE'
    Writing 'lm_stan.Rd'
    Writing 'rstanlm-package.Rd'

## Install and use

Finally, the package is ready to be installed:

    # using ../rstanlm because already inside the rstanlm directory
    install.packages("../rstanlm", repos = NULL, type = "source")

    Installing package into '/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmpg6xLFd/Rinst1119515bc46b5'
    (as 'lib' is unspecified)

It is also possible to use `devtools::install(quick=FALSE)` to install the package. The argument `quick=FALSE` is necessary if you want to recompile the Stan models. Going forward, if you only make a change to the R code or the documentation, you can set `quick=TRUE` to speed up the process, or use `devtools::load_all()`.

After installation, the package can be loaded and used like any other R package:

    library("rstanlm")

    fit <- lm_stan(y = rnorm(10), x = rnorm(10),
                   # arguments passed to sampling
                   iter = 2000, refresh = 500)

    SAMPLING FOR MODEL 'lm' NOW (CHAIN 1).
    Chain 1:
    Chain 1: Gradient evaluation took 8.1e-05 seconds
    Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.81 seconds.
    Chain 1: Adjust your expectations accordingly!
    Chain 1:
    Chain 1:
    Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
    Chain 1: Iteration:  500 / 2000 [ 25%]  (Warmup)
    Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
    Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
    Chain 1: Iteration: 1500 / 2000 [ 75%]  (Sampling)
    Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
    Chain 1:
    Chain 1:  Elapsed Time: 0.341 seconds (Warm-up)
    Chain 1:                0.372 seconds (Sampling)
    Chain 1:                0.713 seconds (Total)
    Chain 1:

    SAMPLING FOR MODEL 'lm' NOW (CHAIN 2).
    Chain 2:
    Chain 2: Gradient evaluation took 4e-05 seconds
    Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
    Chain 2: Adjust your expectations accordingly!
    Chain 2:
    Chain 2:
    Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
    Chain 2: Iteration:  500 / 2000 [ 25%]  (Warmup)
    Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
    Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
    Chain 2: Iteration: 1500 / 2000 [ 75%]  (Sampling)
    Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
    Chain 2:
    Chain 2:  Elapsed Time: 0.387 seconds (Warm-up)
    Chain 2:                0.351 seconds (Sampling)
    Chain 2:                0.738 seconds (Total)
    Chain 2:

    SAMPLING FOR MODEL 'lm' NOW (CHAIN 3).
    Chain 3:
    Chain 3: Gradient evaluation took 3.6e-05 seconds
    Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
    Chain 3: Adjust your expectations accordingly!
    Chain 3:
    Chain 3:
    Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
    Chain 3: Iteration:  500 / 2000 [ 25%]  (Warmup)
    Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
    Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
    Chain 3: Iteration: 1500 / 2000 [ 75%]  (Sampling)
    Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
    Chain 3:
    Chain 3:  Elapsed Time: 0.344 seconds (Warm-up)
    Chain 3:                0.317 seconds (Sampling)
    Chain 3:                0.661 seconds (Total)
    Chain 3:

    SAMPLING FOR MODEL 'lm' NOW (CHAIN 4).
    Chain 4:
    Chain 4: Gradient evaluation took 5.4e-05 seconds
    Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.54 seconds.
    Chain 4: Adjust your expectations accordingly!
    Chain 4:
    Chain 4:
    Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
    Chain 4: Iteration:  500 / 2000 [ 25%]  (Warmup)
    Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
    Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
    Chain 4: Iteration: 1500 / 2000 [ 75%]  (Sampling)
    Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
    Chain 4:
    Chain 4:  Elapsed Time: 0.313 seconds (Warm-up)
    Chain 4:                0.312 seconds (Sampling)
    Chain 4:                0.625 seconds (Total)
    Chain 4:

    print(fit)

    Inference for Stan model: lm.
    4 chains, each with iter=2000; warmup=1000; thin=1;
    post-warmup draws per chain=1000, total post-warmup draws=4000.

               mean se_mean   sd   2.5%   25%   50%   75% 97.5% n_eff Rhat
    intercept  0.32    0.01 0.53  -0.72  0.00  0.32  0.63  1.36  2470 1.00
    beta      -0.65    0.01 0.67  -2.02 -1.03 -0.64 -0.25  0.65  2467 1.00
    sigma      1.58    0.01 0.51   0.91  1.23  1.48  1.79  2.92  1473 1.00
    lp__      -8.19    0.05 1.48 -11.96 -8.82 -7.81 -7.11 -6.52   900 1.01

    Samples were drawn using NUTS(diag_e) at Mon Jan 22 10:10:51 2024.
    For each parameter, n_eff is a crude measure of effective sample size,
    and Rhat is the potential scale reduction factor on split chains (at
    convergence, Rhat=1).

## Advanced options

Details can be found in the documentation for `rstan_create_package()` so we only mention some of these briefly here:

- Running `rstan_create_package()` with `auto_config = TRUE` (the default value) automatically synchronizes the Stan C++ files with the `.stan` model files located in `inst/stan`, although this creates a dependency of your package on rstantools itself (i.e., rstantools must be installed for your package to work). Setting `auto_config = FALSE` removes this dependency, at the cost of having to manually synchronize Stan C++ files by running `rstan_config()` every time a package `.stan` file is added, removed, or even just modified.

- The function `use_rstan()` can be used to add Stan functionality to an existing package, instead of building the package from scratch.

  - Note: If you are already using roxygen in your package, you’ll have to use roxygen to update your Namespace file via the `R/<package-name>-package.R` file. Check the roxygen documentation for more details.

## Adding additional Stan models to an existing R package with Stan models

One may add additional Stan models to an existing package. The following steps are required if one is using `devtools`:

1. Add new Stan file, e.g., `inst/stan/new.stan`
2. Run `pkgbuild::compile_dll()` to preform a fake R CMD install.
3. Run `roxygen2::roxygenize()` to update the documentation.
4. Run `devtools::install()` to install the package locally.

## Links

- Guidelines for Developers of R Packages Interfacing with Stan

- Ask a question at the Stan Forums

- R packages by Hadley Wickham and Jenny Bryan provides a solid foundation in R package development as well as the release process.