# Using Staninside

    library(staninside)

using-staninside.R

# Motivation

‘Stan’ is a powerful language and using R (and specifically ‘rstan’) as glue to allow R users to easily interface existing models. Sometimes a user might wish to use a different backend (‘CmdStanR’) or examine and modify the written ‘Stan’ code directly. This package seeks to allow users the ability to:

- Build R packages that are not pre-compiled by ‘rstan’
- Allow users to use ‘CmdStanR’ in their R packages
- Allow users to easily see the ‘Stan’ code within packages

## Building a Stan Package

### Package Infrastructure

If a package developer were to build a package with ‘staninside’, they first could create the package directory in the usual ways.

Next, to build the appropriate structure the author could call

    temp_dir <- tempdir()
    setup_stan_package(loc = temp_dir)

This function will create the following directory tree:

    .
    ├── inst
    │   ├── stan
    │   │   ├── base.stan
    │   │   ├── data
    │   │   ├── functions
    │   │   ├── parameters

This provides the user with a structure to use when building their ‘Stan’ programs. For complicated programs, it is sometimes useful to break up the ‘Stan’ code blocks into separate files stored in sub-directories. These separate `.stan` files can then be accessed within the ‘Stan’ code with the following syntax:

    # include functions/my_functions.stan

### Model Fitting

Generally speaking, there will be some function in an R package that will fit the data to a model. In order to facilitate this fitting with a ‘CmdStanR’ backend, a process like that below could be used. Note that this model assumes that ‘cmdstanr’ is installed, though this could be replaced with other ways of calling ‘Stan’ (e.g., ‘rstan’).

    fit_my_data <- function(my_data){
        requireNamespace("mypackage")
        local_location <- rappdirs::user_cache_dir(appname = "my_package")

        if (length(list.files(local_location, pattern = ".stan")) > 1) {
            cli::cli_alert_info("Using cached Stan models")
            cli::cli_alert_info(
            "Use `staninside::clear_stan_cache('mypackage')` if you need to refresh")
        } else {
            cli::cli_alert_info("Copying Stan models to cache")
            staninside::copy_models(pkgname = "mypackage", local_location = local_location)
            cli::cli_alert_success("Models copied!")
        }

        model_file_path <- file.path(local_location, paste0("base", ".stan"))
        mod <- cmdstanr::cmdstan_model(model_file_path)

        fit <- mod$sample(data = my_data)

        return(fit)
    }

You now can call `fit_my_data` to fit your model. The underlying ‘Stan’ code is stored in the user cache directory and will be compiled on the first model fitting operation.