Description
Using R for Numerical Analysis in Science and Engineering
Chapman & Hall/CRC The R Series
Author: Bloomfield Victor A.
Language: English
Subjects for Using R for Numerical Analysis in Science and Engineering:
Keywords
Ordinary Differential Equations; Time Series; Data Frame; Curve Fitting; V1 V2 V3; Interpolation; Orthogonal Polynomials; Eigenfunction; QR Decomposition; Eigenvalue; Residual Standard Error; Optimization; NIST Website; Numerical Integration; Plotrix Package; Numerical Differentiation; Gaussian Wave Packet; Nonlinear Model; Spectral Density Estimation; Linear Model; Improved Euler Method; Statistical Analysis; ReacTran Package; Partial Differential Equation; Absolute Error; Differential Equation; Clenshaw Curtis Quadrature; Nonlinear Equation; Euler Method; Linear Equation; Cellular Automata; Deterministic; Min 1Q Median 3Q Max; Stochastic; Grid Cells; Monte Carlo; Gauss Kronrod Quadrature; Numerical Method; Delay Differential Equations; Analysis Using R; Diffusion Advection Equation; R Analysis; Taylor’s Series; R Code; Laguerre Polynomials; R Language; Fresnel Integrals; R Graph; Consistent Initial Values; R Function; R Program; R Software; Base R
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/li>Contents
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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.
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.
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.
