- Project website and documentation: https://konrad1991.github.io/ast2ast/
- I gave a talk at useR! 2022 about ast2ast. The recording is available here: https://m.youtube.com/watch?v=5NDPOLunQTA&list=PL77T87Q0eoJjvKVFHuJZ5_BGVbPPpB8LL&index=8
ast2ast translates an R function into a C++ function. The user can either obtain:
- an external pointer to the compiled C++ function, or
- a regular R function that wraps the compiled C++ code.
To install the development version from GitHub (including vignettes):
devtools::install_github("Konrad1991/ast2ast", build_vignettes = TRUE)Many scientific and numerical applications require calling the same function very often—for example:
- ODE solvers
- optimization routines
- Monte Carlo simulations
- sensitivity analysis
- root finding
- likelihood evaluations
In these scenarios, even small overheads can accumulate and become a bottleneck.
One solution is to rewrite the function in C or C++, but this comes with significant drawbacks:
- steep learning curve
- long development time
- harder debugging
- loss of R’s expressive syntax
ast2ast provides a third way:
✨ Write your function in R — run it at C++ speed.
- Scalars
- Vectors
- Matrices
for,while,repeatif,else if,elsebreak,next
+,-,*,/,^==,!=,<,>,<=,>=&&,||,&,|
sin,asin,sinhcos,acos,coshtan,atan,tanhlog,sqrt,exp
numeric,integer,logicalvector,matrix,rep,c,:length,dimis.na,is.nan,is.finite,is.infiniteprint
[]and[[ ]]
- Catmull–Rom spline via
cmr()
- Forward mode:
seed(),unseed(),get_dot() - Reverse mode:
deriv()
The translated code is often orders of magnitude faster than native R and approaches the performance of handwritten C++.
Below is a benchmark comparing R, C++, and ast2ast-generated code for solving a simple ODE system:
The full code for this benchmark can be found in the vignettes.
-
Main API documentation for
translate(): https://konrad1991.github.io/ast2ast/translate.html -
Full language reference and semantics: https://konrad1991.github.io/ast2ast/DetailedDocumentation.html
-
Guide for package authors: https://konrad1991.github.io/ast2ast/InformationForPackageAuthors.html
ast2ast includes a built-in automatic differentiation (AD) engine with both:
- forward mode
- reverse mode
The reverse-mode engine is adapted and refactored from the open-source autodiff library by Allan Leal (MIT license). It has been extended to:
- integrate with ast2ast’s scalar type system (
Logical,Integer,Double,Dual) - support expression-level dispatch
- interoperate with generated C++20 code
- provide explicit, user-controlled derivative logic
- align error handling and memory semantics with the ast2ast runtime
Unlike many AD frameworks, ast2ast follows an explicit differentiation model:
users assemble derivative computations themselves. This makes performance transparent and predictable.
ast2ast is not a black-box JIT compiler. It is designed for:
- Transparency – you see what is differentiated and how
- Reproducibility – static typing and explicit semantics
- Performance – zero-overhead abstractions in generated C++
- Interoperability – callable from R and C++
- Scientific workflows – built for real numerical code
Contributions are warmly appreciated — whether bug reports, feature requests, documentation improvements, or pull requests.
Please see the Code of Conduct.