Using R for Numerical Analysis in Science and Engineering

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.

"… 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

• 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.