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: |
2.0.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
ggplot2, purrr, tidyr, dplyr, formula.tools, GGally, rlang, utils, tibble, tidyselect |
| Suggests: |
knitr, rmarkdown |
| Published: |
2026-01-16 |
| DOI: |
10.32614/CRAN.package.demodelr |
| Author: |
John Zobitz  [aut,
cre] |
| Maintainer: |
John Zobitz <zobitz at augsburg.edu> |
| BugReports: |
https://github.com/jmzobitz/demodelr/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/jmzobitz/demodelr |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| In views: |
DifferentialEquations |
| CRAN checks: |
demodelr results |
Documentation:
Downloads:
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