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Simulation for Data Science with R

You're reading from   Simulation for Data Science with R Effective Data-driven Decision Making

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785881169
Length 398 pages
Edition 1st Edition
Languages
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Author (1):
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Matthias Templ Matthias Templ
Author Profile Icon Matthias Templ
Matthias Templ
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Table of Contents (13) Chapters Close

Preface 1. Introduction 2. R and High-Performance Computing FREE CHAPTER 3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions 4. Simulation of Random Numbers 5. Monte Carlo Methods for Optimization Problems 6. Probability Theory Shown by Simulation 7. Resampling Methods 8. Applications of Resampling Methods and Monte Carlo Tests 9. The EM Algorithm 10. Simulation with Complex Data 11. System Dynamics and Agent-Based Models Index

The R statistical environment


R was founded by Ross Ihaka and Robert Gentlemen in 1994/1995. It is based on S, a programming language developed by John Chambers (Bell Laboratories), and Scheme. Since 1997, it has been internationally developed and distributed from Vienna over the Comprehensive R Archive Network (CRAN). R is nowadays the most popular and most used software in the statistical world. In addition, R is free and open source (under the GPL2). R is not only a statistical software, it is an environment for interactive computing with data supporting facilities to produce high-quality graphics. The exchange of code with others is easy since everybody can download R. This might also be one reason why modern methods are often exclusively developed in R. R is an object-oriented programming language and has interfaces to many other software products such as C, C++, Java, and interfaces to databases.

Useful information can be found on the following links.

You have been reading a chapter from
Simulation for Data Science with R
Published in: Jun 2016
Publisher: Packt
ISBN-13: 9781785881169
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