The goal of readsdr is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by R libraries. The package accomplishes this goal by parsing .xmile files (Vensim and Stella models) into R objects to construct networks (graph theory), ODE functions for deSolve and Stan.
You can install the released version of readsdr from CRAN with:
install.packages("readsdr")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("jandraor/readsdr")
library(readsdr)
filepath <- system.file("models/", "SIR.stmx", package = "readsdr")
mdl <- read_xmile(filepath)
For reading Vensim models, they must be exported as .xmile.
For information on how to use this package, please check:
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2 These functions cannot be part of more complex mathematical expressions. That is, the auxiliary variable must only contain one smoothing function and nothing else.
3 Seed is ignored.
uniflow and non-negative stock features from Stella are not supported.
No built-in is supported for translations to Stan code.
Modules from Stella are not supported.
This package has been instrumental in the following works:
Andrade & Duggan (2023). Anchoring the mean generation time in the SEIR to mitigate biases in $\Re_0$ estimates due to uncertainty in the distribution of the epidemiological delays. Royal Society Open Science.
Andrade & Duggan (2022). Inferring the effective reproductive number from deterministic and semi-deterministic compartmental models using incidence and mobility data. PLOS Computational Biology.
Andrade & Duggan (2021). A Bayesian approach to calibrate system dynamics models using Hamiltonian Monte Carlo. System Dynamics Review.
Andrade & Duggan (2020). An evaluation of Hamiltonian Monte Carlo performance to calibrate age-structured compartmental SEIR models to incidence data. Epidemics.
Thanks to:
Duggan, J. (2016). System Dynamics Modeling with R. Springer.
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