*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.

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:

- The Lotka–Volterra Equations (31 May 2020)
- Matrices in the Machine (08 Feb 2020)
- Zeroes Are Hard to Find (05 Feb 2020)
- Gradient Fields Forever (25 Feb 2019)
- Errata for Computational Methods for Numerical Analysis (23 Mar 2018)
- Computational Methods for Numerical Analysis is Out Now! (13 Jun 2017)
- Announcing Computational Methods for Numerical Analysis (20 Sep 2016)
- NaN versus NA in R (18 Jul 2016)
- The Wave Equation in R (29 Feb 2016)
- Code Coverage Counterexamples (11 Feb 2016)
- CMNA v0.1.0 Released (01 Feb 2016)
- Euler Method in R for the Initial Value Problem (15 Jan 2016)
- Numerical Analysis talk at Statistical Programming DC (09 Dec 2015)
- How Many Floating Point Numbers are There? (09 Sep 2015)
- Matrix Row Echelon Form in R (20 Apr 2015)

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