Package: demodelr 1.1.0

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>.

Authors:John Zobitz [aut, cre]

demodelr_1.1.0.tar.gz
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demodelr.pdf |demodelr.html✨
demodelr/json (API)
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Bug tracker:https://github.com/jmzobitz/demodelr/issues

On CRAN:

8 exports 4 stars 1.10 score 60 dependencies 10 scripts 205 downloads

Last updated 7 months agofrom:73ca8b50ef. Checks:OK: 7. Indexed: yes.

Exports:compute_likelihoodeigenvalueseulereuler_stochasticmcmc_analyzemcmc_estimatephaseplanerk4

Dependencies:backportsbitbit64broombroom.helperscardsclicliprcolorspacecpp11crayondplyrfansifarverforcatsformula.toolsgenericsGGallyggplot2ggstatsgluegtablehavenhmsisobandlabelinglabelledlatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmeoperator.toolspatchworkpillarpkgconfigplyrprettyunitsprogresspurrrR6RColorBrewerRcppreadrrlangscalesstringistringrtibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr