Features

• Discusses common numerical methods used by scientists and engineers
• Provides practical examples of code, both simple and more complex
• Shows how to produce most standard graphs of data and functions
• Addresses all necessary aspects of the R programming language
• Introduces the most important add-on packages and functions in base R
• Presents the most useful methods for scientific data analysis
• Covers Monte Carlo and stochastic as well as deterministic methods
  

Summary

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also:

• Explains how to statistically analyze and fit data to linear and nonlinear models
• Explores numerical differentiation, integration, and optimization
• Describes how to find eigenvalues and eigenfunctions
• Discusses interpolation and curve fitting
• Considers the analysis of time series
  
  

Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.