demodelr: 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>.

Version: 1.0.1
Depends: R (≥ 4.1.0)
Imports: ggplot2, purrr, tidyr, dplyr, formula.tools, GGally, rlang, utils, tibble
Suggests: knitr, rmarkdown
Published: 2022-09-16
Author: John Zobitz ORCID iD [aut, cre]
Maintainer: John Zobitz <zobitz at augsburg.edu>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: demodelr results

Documentation:

Reference manual: demodelr.pdf

Downloads:

Package source: demodelr_1.0.1.tar.gz
Windows binaries: r-devel: demodelr_1.0.0.zip, r-release: demodelr_1.0.1.zip, r-oldrel: demodelr_1.0.1.zip
macOS binaries: r-release (arm64): demodelr_1.0.1.tgz, r-oldrel (arm64): demodelr_1.0.1.tgz, r-release (x86_64): demodelr_1.0.1.tgz, r-oldrel (x86_64): demodelr_1.0.1.tgz
Old sources: demodelr archive

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