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readsdr

Overview

The goal of readsdr is to bridge the design capabilities fromspecialised System Dynamics software with the powerful numerical toolsoffered by R libraries. The package accomplishes this goal by parsing.xmile files (Vensim andStella models) into R objects toconstructnetworks (graph theory), ODEfunctions fordeSolve andStan.

Installation

You can install the released version of readsdr fromCRAN with:

install.packages("readsdr")

And the development version fromGitHub with:

# install.packages("devtools")devtools::install_github("jandraor/readsdr")

Usage

library(readsdr)filepath<-system.file("models/","SIR.stmx",package ="readsdr")mdl<-read_xmile(filepath)

For reading Vensim models, they must be exported as .xmile.

Tutorials

For information on how to use this package, please check:

• Basics

• Inference

Supported builtins

• Stella
  • Comparison operators (=,<>)
  • Logical operators (AND,OR,NOT)
  • If Else Then
  • Inequality operators (<,>)
  • Pulse¹
  • Step¹
  • Endogenous table functions
  • Smoothing functions²:SMTH1,SMTH3,SMTHN
  • Delay functions:DELAYN
  • Limited support touni-dimensional arrays.Applyall translation is restricted to numeric values.
  • Math functions:ABS,SQRT
  • Stats functions:NORMAL³
• Vensim
  • Comparison operators (=,<>)
  • Logical operators (:AND:,:OR:,:NOT:)
  • IF_THEN_ELSE
  • Inequality operators (<,>)
  • Pulse¹
  • Pulse Train¹
  • Step¹
  • Endogenous table functions
  • Smoothing functions²:SMOOTH,SMOOTH3,SMOOTH3I,SMOOTHI
  • Delay functions:DELAY N
  • Math functions:ABS,SQRT
  • Limited support tobi-dimensional arrays.
  • Stats functions:RANDOM NORMAL³

¹ Restricted to Euler integration.

² These functions cannot be part of more complexmathematical expressions. That is, the auxiliary variable must onlycontain one smoothing function andnothing else.

³ Seed is ignored.

Notes

• uniflow andnon-negative stock features fromStella arenot supported.

• No built-in is supported for translations toStancode.

• Modules fromStella arenotsupported.

Applications

This package has been instrumental in the following works:

• Andrade &Duggan (2023).Anchoring the mean generation time in the SEIR tomitigate biases in\(\Re_0\)estimates due to uncertainty in the distribution of theepidemiological delays.Royal Society OpenScience.

• Andrade& Duggan (2022).Inferring the effective reproductive numberfrom deterministic and semi-deterministic compartmental models usingincidence and mobility data.PLOS ComputationalBiology.

• Andrade & Duggan(2021).A Bayesian approach to calibrate system dynamics modelsusing Hamiltonian Monte Carlo.System DynamicsReview.

• Andrade& Duggan (2020).An evaluation of Hamiltonian Monte Carloperformance to calibrate age-structured compartmental SEIR models toincidence data.Epidemics.

Acknowledgments

Thanks to:

• Rogelio Oliva foradvocating the need to create an open-source tool for the SystemDynamics community and push forward this endeavour.
• Sergey Naumov for his ideas to implement several functions in thispackage.
• Jim Duggan from whom Iborrowed ideas to implement this package.

References

Duggan,J. (2016).System Dynamics Modeling with R. Springer.