Computational Methods for Numerical Analysis with R | James Howard Computational Methods for Numerical Analysis with R | James Howard

# Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R (CMNA) is a treatment of the traditional numerical analysis course using R as the underlying programming language. The traditional numerical analysis outline begins with numerical error, then linear algebra, interpolation, integration, optimization, and differential equations. In this way, the outline covers the entire introductory mathematical sequence. This text will be suitable for the advanced undergraduate student or first-year graduate student. The book will require a solid understanding of linear algebra, differential and integral calculus, and differential equations. Students of mathematics, computer science, physics, engineering, and other mathematically intensive disciplines will have sufficient background to read and understand the book.

My motivation for writing this book is the lack of similar materials in the market. There are several commonly used textbooks that teach numerical analysis using MATLAB. Others use C or Fortran. MATLAB is a very expensive program and while available on college campuses, is not cheap enough for most graduates to purchase. C and Fortran are inexpensive or free, but require basic programming skills to manage data input and output, memory, and other tasks that should not be set upon someone trying to learn a specific set of skills. R provides a rich environment that students are already familiar with due to its rapidly growing user base. It is free for all users and it does not require intensive “environmental management” when programming, as is required in, for instance, Java.

## Software

The cmna R package is available containing all of the algorithms from this book, and a few extra implementations of interest. You can access the source code for the cmna package at GitHub. Install cmna via DevTools:

The cmna package is developed using the Gitflow development workflow. To install the development branch, use:

## Bibliographic Information

1. James P. Howard, II, Computational Methods for Numerical Analysis with R, ser. Numerical Analysis and Scientific Computing. New York: Chapman and Hall/CRC, 2017.