Using R for Numerical Analysis in Science and Engineering
1st Edition
Published on 3. September 2018
359 pages
978-1-315-36049-2 (ISBN)
Description
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.
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.
Reviews / Votes
"... 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
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
Illustrations
133 Illustrations, black and white
File size
11,99 MB
ISBN-13
978-1-315-36049-2 (9781315360492)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Victor A. Bloomfield
Using R for Numerical Analysis in Science and Engineering
Book
04/2014
1st Edition
Chapman & Hall/CRC
€124.50
Shipment within 15-20 days
Person
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.
Content
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.
