Model systems as stock-and-flow models in R. Stock-and-flow models are foundational to system dynamics, and help to understand systems complicated by nonlinearities, delays, and feedback loops. sdbuildR aims to make stock-and-flow modeling accessible and effortless, enabling you to dedicate your expertise to what matters most: building insightful, high-quality models. Get started at https://kcevers.github.io/sdbuildR/!
- Accessibility: Get started with stock-and-flow models with limited knowledge.
- Flexibility: Modify your models easily and robustly.
- Insight Maker integration: Import models from Insight Maker.
- Julia backend: Use Julia for high-performance simulations, without any knowledge of Julia.
- Unit support: Use standard or custom units to verify and interpret your model.
All package capabilities are described in the vignettes:
- Build: Learn how to build, modify, and debug stock-and-flow models.
- Julia setup: Install and set up the Julia environment for running ensemble simulations and using units.
- Ensemble: Learn how to assess a model’s sensitivity, uncertainty and robustness with ensemble simulations.
- Units: Learn why and how to use (custom) units.
The release version can be installed from CRAN:
install.packages("sdbuildR")The development version can be installed from GitHub:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("KCEvers/sdbuildR")sdbuildR offers two simulation engines: R and Julia (supported by JuliaConnectoR). If you would like to run ensemble simulations and use units, you will need to install and set up the Julia environment. See this vignette for guidance.
To cite sdbuildR, please use:
citation("sdbuildR")
#> To cite package 'sdbuildR' in publications use:
#>
#> Evers K (2025). _sdbuildR: Easily Build, Simulate, and Visualise
#> Stock-and-Flow Models_. R package version 1.0.3,
#> https://github.com/KCEvers/sdbuildR,
#> <https://kcevers.github.io/sdbuildR/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {sdbuildR: Easily Build, Simulate, and Visualise Stock-and-Flow Models},
#> author = {Kyra Caitlin Evers},
#> year = {2025},
#> note = {R package version 1.0.3, https://github.com/KCEvers/sdbuildR},
#> url = {https://kcevers.github.io/sdbuildR/},
#> }-
Unlike in other system dynamics software, sdbuildR provides only minimal support for non-negative stocks and flows. Specifically, setting stocks to non-negative will constrain the stocks to remain non-negative, but will not adjust the corresponding flows. In any case, enforcing either stocks or flows to be non-negative is not recommended, as it may mask model misspecification. Stocks and flows that logically cannot be negative (e.g., animals or deaths) should ideally remain non-negative as a result of the model’s equations and parameters, rather than by forcing them to be non-negative.
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sdbuildR does not support vectorized operations, destructuring assignment, or minimum and maximum constraints for variables.
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sdbuildR does not support the Insight Maker functions Stop(), Prompt(), Confirm(), Pause(), Fix(), Map(), Filter(), and Repeat(), nor the delay and past functions. A message is issued if any of these are detected.
sdbuildR is heavily based on common system dynamics software such as Vensim, Powersim, Stella, and Insight Maker. To translate xmile models to R, see the R package readsdr. To build stock-and-flow models with the R package deSolve, the book System Dynamics Modeling with R by Jim Duggan will prove useful. In Python, stock-and-flow models are supported by PySD.
sdbuildR is under active development. While thoroughly tested, the
package may have bugs, particularly in complex model translations. We
encourage users to report issues on
GitHub - your input helps
the package improve! Use debugger() to diagnose model errors, and use
the vignettes for guidance.