One of the coolest things about working on a project like Stan has been seeing some of our users begin to develop tools for making Stan more accessible to audiences that may otherwise not benefit from what Stan offers. In particular, recently we have started seeing a growing number of R packages that provide high-level interfaces to Stan, using the rstan package for estimating models without requiring that the user be familiar with the Stan modeling language itself.
This is a great development and we would like to support such efforts going forward, but to-date we have made little effort to coordinate the development of these packages. To avoid a Wild West, so to speak, of Stan-based R packages, we think it is important that developers make every effort to adhere to certain guidelines in order to ensure these packages are of the highest possible quality and provide the best possible experience for users. To that end, in this post we present a set of recommendations for the development of R packages that interface with Stan. These recommendations are based on software design principles we value as well as many things we are learning as we continue developing our own packages and review packages being developed by others. There are exceptions to some of these recommendations (e.g., the brms package is a sensible exception to one of guidelines about Stan code), but we strongly recommend trying to follow them whenever possible.
These recommendations are not set in stone. We expect them to evolve and we very much appreciate feedback on how they can be improved. And, of course, we look forward to seeing the packages you develop using Stan, so please let us know about them!
The rstantools package provides the
rstan_create_package() function, which you should use to
create the basic structure of your package. (As of
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.
All Stan code for estimating models should be included in
.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
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.
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
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.
package serves as the back-end for plotting for
rstanarm (see for example
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.
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.