Using Staninside

library(staninside)

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.