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demodelr  

Simulating Differential Equations with Data
View on CRAN: Click here


Download and install demodelr package within the R console
Install from CRAN:
install.packages("demodelr")

Install from Github:
library("remotes")
install_github("cran/demodelr")

Install by package version:
library("remotes")
install_version("demodelr", "1.0.1")



Attach the package and use:
library("demodelr")
Maintained by
John Zobitz
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-06-23
Latest Update: 2022-09-16
Description:
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) .
How to cite:
John Zobitz (2022). demodelr: Simulating Differential Equations with Data. R package version 1.0.1, https://cran.r-project.org/web/packages/demodelr
Previous versions and publish date:
1.0.0 (2022-06-23 08:00)
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