Exploring Modeling with Data and Differential Equations Using R - Zobitz, John

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Résumé :
This book provides a unique introduction to differential equations with applications to the biological and other natural sciences. Additionally, model parameterization and simulation of stochastic differential equations are explored, providing additional tools for model analysis and evaluation...

Biographie:
the text's integrated framework is a stepping stone for further advanced study in mathematics or as a comprehensive introduction to modeling for quantitative natural scientists.

The text will introduce you to:

• modeling with systems of differential equations and developing analytical, computational, and visual solution techniques.
• the R programming language, the tidyverse syntax, and developing data science workflows.
• qualitative techniques to analyze a system of differential equations.
• data assimilation techniques (simple linear regression, likelihood or cost functions, and Markov Chain, Monte Carlo Parameter Estimation) to parameterize models from data.
• simulating and evaluating outputs for stochastic differential equation models.

An associated R package provides a framework for computation and visualization of results. It can be found here: https://cran.r-project.org/web/packages/demodelr/index.html.