Computational Methods for Numerical Analysis with R

James P Howard II

ISBN 10: 0367657910 / ISBN 13: 9780367657918

Published by Chapman And Hall/CRC Sep 2020, 2020

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R, along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R, but are interested in learning more about how the underlying algorithms work. As such, it is suitable for statisticians, economists, and engineers, and others with a computational and numerical background. 280 pp. Englisch.

Seller Inventory # 9780367657918

Bibliographic Details

• Title: Computational Methods for Numerical Analysis with R
• Publisher: Chapman And Hall/CRC Sep 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 0367657910
• ISBN 13: 9780367657918
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 15D millimeters 234 millimeters width by 156 millimeters height by 15 millimeters depth
• Weight: 430 grams

About this title

Synopsis

Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R, along with examples to demonstrate the function and its use.

Computational Methods for Numerical Analysis with R is intended for those who already know R, but are interested in learning more about how the underlying algorithms work. As such, it is suitable for statisticians, economists, and engineers, and others with a computational and numerical background.

About the Author

James P Howard, II