Multiple Comparisons Using R

Reviews / Votes

The book is primarily targeted at practitioners who will find the illustrative examples utilizing real data helpful. Methodologically interested readers are provided with interesting concepts and a good survey on multiple comparisons literature in the bibliography.
-Thorsten Dickhaus, Biometrics, September 2012

Nice stand-out features of the book are its clarity and conciseness, its coverage of group sequential and adaptive designs, and the nice illustration through actual problems using R software and the CRAN library of R routines for multiple testing. ... useful for statistical consultants and graduate students who perhaps cannot afford a SAS license. ... Another nice feature of the book is its organization.
-Michael R. Chernick, Technometrics, August 2011

An excellent first chapter gently explains to the reader why multiple comparison techniques are required. Successive chapters delve into more and more sophisticated scenarios for multiple comparisons. The benefits of applying multiple testing in the context of ordinary linear models are in my opinion not often stressed in undergraduate statistics courses. It was refreshing to see an entire chapter devoted to this idea. The over 150 references make this an excellent entry point into the literature ... Do consider it [the book] if you are a user of R, who is also a researcher or teacher of linear modelling, and needs to apply multiple comparisons in their work. Your work will be the better for it.
-Alice Richardson, International Statistical Review (2011), 79

I found the book to be a rare gem; it is very seldom indeed that a reviewer has difficulty finding things to criticize. The book is concise, but covers all the common topics and a little more. It is simply written, yet manages to explain the key concepts remarkably well. Above all, it introduces a comprehensible structure that unifies seemingly different methodologies into a single system. ... I believe this book could be useful to all those seeking guidance [on] the multitude of multiplicity correcting procedures. ... The authors managed to create a fine balance between theory and practical applications ... The book is warmly recommended.
-Vera Lisovskaja, Journal of Biopharmaceutical Statistics, Vol. 21, 2011 The book is primarily targeted at practitioners who will find the illustrative examples utilizing real data helpful. Methodologically interested readers are provided with interesting concepts and a good survey on multiple comparisons literature in the bibliography.
-Thorsten Dickhaus, Biometrics, September 2012

Nice stand-out features of the book are its clarity and conciseness, its coverage of group sequential and adaptive designs, and the nice illustration through actual problems using R software and the CRAN library of R routines for multiple testing. ... useful for statistical consultants and graduate students who perhaps cannot afford a SAS license. ... Another nice feature of the book is its organization.
-Michael R. Chernick, Technometrics, August 2011

An excellent first chapter gently explains to the reader why multiple comparison techniques are required. Successive chapters delve into more and more sophisticated scenarios for multiple comparisons. The benefits of applying multiple testing in the context of ordinary linear models are in my opinion not often stressed in undergraduate statistics courses. It was refreshing to see an entire chapter devoted to this idea. The over 150 references make this an excellent entry point into the literature ... Do consider it [the book] if you are a user of R, who is also a researcher or teacher of linear modelling, and needs to apply multiple comparisons in their work. Your work will be the better for it.
-Alice Richardson, International Statistical Review (2011), 79

I found the book to be a rare gem; it is very seldom indeed that a reviewer has difficulty finding things to criticize. The book is concise, but covers all the common topics and a little more. It is simply written, yet manages to explain the key concepts remarkably well. Above all, it introduces a comprehensible structure that unifies seemingly different methodologies into a single system. ... I believe this book could be useful to all those seeking guidance [on] the multitude of multiplicity correcting procedures. ... The authors managed to create a fine balance between theory and practical applications ... The book is warmly recommended.
-Vera Lisovskaja, Journal of Biopharmaceutical Statistics, Vol. 21, 2011