R and MATLAB (Chapman & Hall/CRC The R Series) By David E. Hiebeler

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R and MATLAB (Chapman & Hall/CRC The R Series)
 By David E. Hiebeler

R and MATLAB (Chapman & Hall/CRC The R Series) By David E. Hiebeler


R and MATLAB (Chapman & Hall/CRC The R Series)
 By David E. Hiebeler


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R and MATLAB (Chapman & Hall/CRC The R Series)
 By David E. Hiebeler

  • Sales Rank: #1347178 in Books
  • Published on: 2015-06-02
  • Original language: English
  • Number of items: 1
  • Dimensions: 10.00" h x .70" w x 7.10" l, .0 pounds
  • Binding: Hardcover
  • 233 pages

Review

"… there is more to the book than simply a guide for translating from one computing environment to the other. The author also provides many useful suggestions for effective use of both R and MATLAB. Furthermore, in many places, the author explains what each environment is actually doing when a command or routine is called. This is useful because it can serve as an indicator as to whether R or MATLAB is the more appropriate choice for a given computing task. … a highly valuable resource for anyone currently using or intending to use either. … I personally welcome the existence of this book and am very grateful to the author for putting in the work to write it. R and MATLAB is well written and should be accessible to students and researchers alike."
―MAA Reviews, December 2015

"I find this text to be an important reference for many researchers (especially those in highly collaborative environments) that only know one of these two languages. In this situation, I believe this text to be an essential reference as it will make working with your collaborators more efficient. Even if you are well versed in both of these languages, I think this text can help you save time converting between the two. " (The American Statistician)

About the Author

David E. Hiebeler is an associate professor in the Department of Mathematics & Statistics at the University of Maine. He earned a PhD in applied mathematics from Cornell University. His research involves mathematical and computational stochastic spatial models in population ecology and epidemiology.

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