Guidelines for Developers of R Packages Interfacing with Stan

General package structure and development

• The rstantools package provides the rstan_create_package() function, which you should use to create the basic structure of your package. (As of v2.0.0 this replaces the rstan_package_skeleton function.) This will set up a package with functionality for pre-compiled Stan programs, in the style of the rstanarm package (source code: https://github.com/stan-dev/rstanarm).

• Use version control (e.g., git).

• Unless you are developing proprietary private software, organize your code in a repository that is public on GitHub (or a similar service, but preferably GitHub). It should be public even at early stages of development, not only when officially released. We recommend you add a note to your README file on how to install the development version of your package, like in the bayesplot README

• Unit testing is essential. There are several R packages that make it relatively easy to write tests for your package. Most of our R packages (e.g., rstanarm, brms, bayesplot, shinystan, loo and others) use the testthat package for this purpose, but if you prefer a different testing framework that’s fine. The covr package is useful for calculating the line coverage of your tests, and we recommend reaching a high level of coverage before releasing a package. Good line coverage does not guarantee high quality tests, but it’s a good first step. We also recommend setting up automatic testing of your package using GitHub Actions. See https://github.com/r-lib/actions for useful templates.

Stan code

• All Stan code for estimating models should be included in pre-written static .stan files that are compiled when the package is built (see the Stan programs directory in the rstanarm repo for examples). You can also use subdirectories to include code chunks to be used in multiple .stan files (again see the rstanarm repo for examples). If you set up your package using rstan_create_package this structure will be created for you. This means that your package should NOT write a Stan program when the user calls a model fitting function in your package, but rather use only Stan programs you have written by hand in advance (if you are working on a model for which you don’t think this is possible please let us know). There are several reasons for this.

• Pre-compiled Stan programs can be run by users of Windows or Mac OSX without having to install a C++ compiler, which dramatically expands the universe of potential users for your package.

• Pre-compiled Stan programs will run immediately when called, avoiding compilation time.

• CRAN policy permits long installation times but imposes restrictions on the time consumed by examples and unit tests that are much shorter than the time that it takes to compile even a simple Stan program. Thus, it is only possible to adequately test your package if it has pre-compiled Stan programs.

• Pre-compiled Stan programs can use custom C++ functions.

To provide flexibility to users, your Stan programs can include branching logic (conditional statements) so that even with a small number of .stan files you can still allow for many different specifications to made by the user (see the .stan files in rstanarm for examples).

• Use best practices for Stan code. If the models you intend to implement are discussed in the Stan manual or on the Stan users forum then you should follow any recommendations that apply to your case. If you are unsure whether your Stan programs can be made more efficient or more numerically stable then please ask us on the Stan users forum. Especially ask us if you are unsure whether your Stan programs are indeed estimating the intended model.

• Relatedly, prioritize safety over speed in your Stan code and sampler settings. For example, if you can write a program that runs faster but is potentially less stable, then at a minimum you should make the more stable version the default. This also means that, with rare exceptions, you should not change our recommended MCMC defaults (e.g. 4 chains, 1000+1000 iterations, NUTS not static HMC), unless you are setting the defaults to something more conservative. rstanarm even goes one step further, making the default value of the adapt_delta tuning parameter at least 0.95 for all models (rather than rstan’s default of 0.8) in order to reduce the step size and therefore also limit the potential for divergences. This means that rstanarm models may often run a bit slower than they need to if the user doesn’t change the defaults, but it also means that users face fewer situations in which they need to know how to change the defaults and what the implications of changing the defaults really are.

R code and documentation

• Functions/methods that provide useful post-estimation functionality should be given the same names as the corresponding functions in rstanarm (if applicable). For example, posterior_predict() to draw from the posterior predictive distribution, posterior_interval() for posterior uncertainty intervals, etc. To make this easier, these and similar rstanarm functions have been converted to S3 methods for the stanreg objects created by rstanarm and the S3 generic functions are included here in the rstantools package. Your package should import the generics from rstantools for whichever functions you want to include in your package and then provide methods for the fitted model objects returned by your model-fitting functions. For some other functions (e.g. as.matrix) the generics are already available in base R or core R packages. To be clear, we are not saying that the naming conventions used in rstanarm/rstantools are necessarily optimal. (If you think that one of our function names should be changed please let us know and suggest an alternative. If it is a substantial improvement we may consider renaming the function and deprecating the current version.) Rather, this guideline is intended to make function names consistent across Stan-based R packages, which will improve the user experience for those users who want to take advantage of a variety of these packages. It will be a mess if every R package using Stan has different names for the same functionality.

• The bayesplot package serves as the back-end for plotting for rstanarm (see for example pp_check.stanreg and plot.stanreg), brms, and other packages, and we hope developers of other Stan-based R packages will also use it. You can see all the other R packages using bayesplot in the Reverse dependencies section of the bayesplot CRAN page. For any plot you intend to include in your package, if it is already available in bayesplot then we recommend using the available version or suggesting (or contributing) a better version. If it is not already available then there is a good chance we will be interested in including it in bayesplot if the plot would also be useful for other developers.

• The posterior package (new in 2021) provides state of the art posterior inference diagnostics, various summaries of draws in convenient formats, and functionality for converting between (and manipulating) many different useful formats of draws from posterior or prior distributions. We recommend using this functionality in your package or recommending it to your users.

• Take documentation seriously. The documentation won’t be perfect (we constantly find holes in the doc for the R packages in the Stan ecosystem), but you should make every effort to provide clear and thorough documentation.

Recommended resources

• Hadley Wickham and Jenny Bryan’s book on R packages. If you are interested in developing an R package that interfaces with Stan but are not an experienced package developer, we recommend this book, which is free to read online. Even if you are an experienced developer of R packages, the book is still a great resource.

• If you need help setting up your package or have questions about these guidelines the best places to go are the Stan Forums and the GitHub issue tracker for the rstantools package.