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Using R for Numerical Analysis in Science and Engineering de Victor A. Bloomfield

Using R for Numerical Analysis in Science and Engineering: Chapman &Hall/CRC The R Series

Autor Victor A. Bloomfield
en Limba Engleză Hardback – 24 apr 2014
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.
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Specificații

ISBN-13: 9781439884485
ISBN-10: 143988448X
Pagini: 360
Ilustrații: 133 black & white illustrations
Dimensiuni: 156 x 234 x 25 mm
Greutate: 0.83 kg
Ediția:New.
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman &Hall/CRC The R Series

Cuprins

Introduction. Calculating. Graphing. Programming and Functions. Solving Systems of Algebraic Equations. Numerical Differentiation and Integration. Optimization. Ordinary Differential Equations. Partial Differential Equations. Analyzing Data. Fitting Models to Data.

Notă biografică

Victor A. Bloomfield is currently emeritus professor at University of Minnesota, Minneapolis, USA. His research has encompassed more than four decades and a variety of topics, including enzyme kinetics, dynamic laser light scattering, bacteriophage assembly, DNA condensation, scanning tunneling microscopy, and single molecule stretching experiments on DNA. His theoretical work on biopolymer hydrodynamics and polyelectrolyte behavior has resulted in over 200 peer-reviewed journal publications. Using R for Numerical Analysis in Science and Engineering is an extension and broadening of his 2009 book, Computer Simulation and Data Analysis in Molecular Biology and Biophysics: An Introduction Using R, for general usage in science and engineering.

Recenzii

"… the book is well organized, clearly written, and has a large amount of useful R code. It does a good job of answering the question of how to use R to perform numerical analyses of interest to scientists and engineers and, as such, can be recommended to the intended audience."
—Journal of the Royal Statistical Society, Series A, 2015
"I would recommend it to those seeking to improve their programming efficiency. … the extensive coverage of optimization, ordinary differential equations, and partial differential equations combined with its exemplary demonstration of R coding through effective examples make this book a valuable resource for a wide audience. … a good reference for scientific and engineering researchers."
—The American Statistician, February 2015

"... the book is well organized, clearly written, and has a large amount of useful R code. It does a good job answering the question of how to use R to perform numerical analyses of interest to scientists and engineers, and as such, can be recommended to the intended audience."
—Andrey Kostenko, Teaching Statistics

Descriere

This practical guide shows how to use R and its add-on packages to obtain numerical solutions to complex mathematical problems commonly faced by scientists and engineers. Providing worked examples and code, the text not only addresses necessary aspects of the R programming language but also demonstrates how to produce useful graphs and statistically analyze and fit data to linear and nonlinear models. It covers Monte Carlo, stochastic, and deterministic methods and explores topics such as numerical differentiation and integration, interpolation and curve fitting, and optimization.