Suchergebnisse

Introduction / Patrick Doreian, Vladimir Batagelj, and AnusÌ⁺ka Ferligoj -- Bibliometric Analyses of the Network Clustering Literature / Vladimir Batagelj, AnusÌ⁺ka Ferligoj, and Patrick Doreian -- Clustering Approaches to Networks / Vladimir Batagelj -- Different Approaches to Community Detection / Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, and Renaud Lambiotte -- Label Propagation for Clustering / Lovro SÌ⁺ubelj -- Blockmodeling of Valued Networks / Carl Nordlund and AlesÌ⁺ ZÌ⁺iberna -- Treating Missing Network Data Before Partitioning / Anja ZÌ⁺nidarsÌ⁺ic, Patrick Doreian, and AnusÌ⁺ka Ferligoj Ë⁷ -- Partitioning Signed Networks / Vincent Traag, Patrick Doreian, and Andrej Mrvar -- Partitioning Multimode Networks / Martin G Everett and Stephen P Borgatti -- Blockmodeling Linked Networks / AlesÌ⁺ ZÌ⁺iberna -- Bayesian Stochastic Blockmodeling / Tiago P. Peixoto -- Structured Networks and Coarse-Grained Descriptions: A Dynamical -- Perspective / Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, and Mauricio Barahona -- Scientific Co-Authorship Networks / Marjan Cugmas, AnusÌ⁺ka Ferligoj, and Luka Kronegger -- Conclusions and Directions for Future Work / Patrick Doreian, AnusÌ⁺ka Ferligoj, and Vladimir Batagelj.

"The dynamic, student focused textbook provides step-by-step instruction in the use of R and of statistical language as a general research tool. It is ideal for anyone hoping to: Complete an introductory course in statistics Prepare for more advanced statistical courses Gain the transferable analytical skills needed to interpret research from across the social sciences Learn the technical skills needed to present data visually Acquire a basic competence in the use of R. The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed pedagogy and jargon-free definitions. Theory is introduced as an accessible and adaptable tool and is always contextualized within the pragmatic context of real research projects and definable research questions"--

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. In the process, you'll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit testsAutomate anything you can, taking advantage of the years of development experience embodied in devtoolsGet tips on good style, such as organizing functions into filesStreamline your development process with devtoolsLearn the best way to submit your package to the Comprehensive R Archive Network (CRAN)Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

"The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data."--Publisher's description