From the reviews: "The book is intended as a means of introducing functional data analysis to those who would like to use it as a research tool in a variety of applications. It gives a brief but clear description of the concepts and methods together with a strong focus on implementation. The mixture of R and RATLAB illustrative code works well and the latter computing environment, together with the material on dynamics, will suit those from an engineering or physical sciences background. It therefore provides an excellent starting point for those who would like to make use of these very powerful techniques in analyzing data." (Journal of Statistical Software, April 2010, Vol. 34, Book Review 3) "This well-written book provides a great, intuitive introduction to functional data analysis ... . I recommend this book for statisticians wanting to learn about the basics of functional data analysis, as well as practitioners wanting to explore their own data and perform some analyses on their own. ... it would be a good basis for an applied course in functional data analysis that could be taken by statistics and biostatistics M.S. and Ph.D. students as well as other scientists with a reasonably deep quantitative background." (Jeffrey S. Morris, The American Statistician, Vol. 65 (4), November, 2011) "The intended audience is anybody performing FDA who must implement or use FDA software. ... The goal is to educate and equip the reader for research in and/or implementation of FDA. I strongly recommend the book and will briefly describe each chapter. Most chapters include exercises so the text could easily be used as a text in a course on FDA. ... In conclusion, this is a very welcome fda book." (Tom Burr, Technometrics, Vol. 52 (4), November, 2010)

• to Functional Data Analysis.- Essential Comparisons of the Matlab and R Languages.- How to Specify Basis Systems for Building Functions.- How to Build Functional Data Objects.- Smoothing: Computing Curves from Noisy Data.- Descriptions of Functional Data.- Exploring Variation: Functional Principal and Canonical Components Analysis.- Registration: Aligning Features for Samples of Curves.- Functional Linear Models for Scalar Responses.- Linear Models for Functional Responses.- Functional Models and Dynamics.

From the reviews: "The book is intended as a means of introducing functional data analysis to those who would like to use it as a research tool in a variety of applications. It gives a brief but clear description of the concepts and methods together with a strong focus on implementation. The mixture of R and RATLAB illustrative code works well and the latter computing environment, together with the material on dynamics, will suit those from an engineering or physical sciences background. It therefore provides an excellent starting point for those who would like to make use of these very powerful techniques in analyzing data." (Journal of Statistical Software, April 2010, Vol. 34, Book Review 3) "This well-written book provides a great, intuitive introduction to functional data analysis ... . I recommend this book for statisticians wanting to learn about the basics of functional data analysis, as well as practitioners wanting to explore their own data and perform some analyses on their own. ... it would be a good basis for an applied course in functional data analysis that could be taken by statistics and biostatistics M.S. and Ph.D. students as well as other scientists with a reasonably deep quantitative background." (Jeffrey S. Morris, The American Statistician, Vol. 65 (4), November, 2011) "The intended audience is anybody performing FDA who must implement or use FDA software. ... The goal is to educate and equip the reader for research in and/or implementation of FDA. I strongly recommend the book and will briefly describe each chapter. Most chapters include exercises so the text could easily be used as a text in a course on FDA. ... In conclusion, this is a very welcome fda book." (Tom Burr, Technometrics, Vol. 52 (4), November, 2010)

• to Functional Data Analysis.- Essential Comparisons of the Matlab and R Languages.- How to Specify Basis Systems for Building Functions.- How to Build Functional Data Objects.- Smoothing: Computing Curves from Noisy Data.- Descriptions of Functional Data.- Exploring Variation: Functional Principal and Canonical Components Analysis.- Registration: Aligning Features for Samples of Curves.- Functional Linear Models for Scalar Responses.- Linear Models for Functional Responses.- Functional Models and Dynamics.