Simulating Differential Equations with Data

Designed to support the visualization, numerical computation, qualitative analysis, model-data fusion, and stochastic simulation for autonomous systems of differential equations. Euler and Runge-Kutta methods are implemented, along with tools to visualize the two-dimensional phaseplane. Likelihood surfaces and a simple Markov Chain Monte Carlo parameter estimator can be used for model-data fusion of differential equations and empirical models. The Euler-Maruyama method is provided for simulation of stochastic differential equations. The package was originally written for internal use to support teaching by Zobitz, and refined to support the text "Exploring modeling with data and differential equations using R" by John Zobitz (2021) < https://jmzobitz.github.io/ModelingWithR/index.html>.

demodelr

demodelr is a R package to support the textbook Exploring Modeling with Data and Differential Equations Using R

Installation

Installation in R is done via CRAN:

install.packages("demodelr")

Also through the devtools package:

install_github("jmzobitz/demodelr", build = TRUE, build_opts = c("--no-resave-data", "--no-manual"),force=TRUE)

If you encounter problems with code in this repository, feel free to post an issue.

License

This work is distributed under the Creative Commons, Attribution-Non Commercial 4.0 License. You may copy, distribute, display and perform the work and make derivative works and remixes based on it only if they give the author (Zobitz) attribution and use it for non-commercial purposes.

Disclaimer

Information and documents contained within this repository are available as-is. Codes or documents, or their use, may not be supported or maintained under any program or service.