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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 1108407129 ISBN 13: 9781108407120
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Published by Cambridge University Press, Cambridge, 2017
ISBN 10: 1108407129 ISBN 13: 9781108407120
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Paperback. Condition: new. Paperback. This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done. This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Cambridge University Press, 2017
ISBN 10: 1108407129 ISBN 13: 9781108407120
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Published by Cambridge University Press 2017-08-10, 2017
ISBN 10: 1108407129 ISBN 13: 9781108407120
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ISBN 10: 1108407129 ISBN 13: 9781108407120
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Published by Springer Nature Switzerland AG, 2020
ISBN 10: 3030441288 ISBN 13: 9783030441289
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Published by Springer Nature Switzerland AG, 2020
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Add to basketPaperback. Condition: New. Second Edition 2020. The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualizationand characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.