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Sprache: Englisch
Verlag: Springer Nature Switzerland AG, 2020
ISBN 10: 3030441288 ISBN 13: 9783030441289
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Sprache: Englisch
Verlag: Springer Nature Switzerland AG, 2020
ISBN 10: 3030441288 ISBN 13: 9783030441289
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Verlag: Springer-Nature New York Inc, 2020
ISBN 10: 3030441288 ISBN 13: 9783030441289
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Sprache: Englisch
Verlag: Springer International Publishing, 2020
ISBN 10: 3030441288 ISBN 13: 9783030441289
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Zustand: New. Presents a fully updated and easily accessible introduction to statistical methods of network analysis and their implementation in RNew edition includes a chapter covering networked experiments and an overhaul to the R code for igraphAc.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2020
ISBN 10: 3030441288 ISBN 13: 9783030441289
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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Taschenbuch. Zustand: Neu. Statistical Analysis of Network Data with R | Eric D. Kolaczyk (u. a.) | Taschenbuch | Use R! | xiv | Englisch | 2020 | Springer | EAN 9783030441289 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.