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Título : Analysis of Integrated and Cointegrated Time Series with R Tipo de documento: documento electrónico Autores: Pfaff, Bernhard ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Use R!, ISSN 2197-5736 Número de páginas: XX, 190 p Il.: online resource ISBN/ISSN/DL: 978-0-387-75967-8 Idioma : Inglés (eng) Palabras clave: Mathematical statistics Probabilities Statistics Econometrics Economics Statistical Theory and Methods Probability Stochastic Processes in Computer Science Clasificación: 51 Matemáticas Resumen: The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics. This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R. The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models. The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes. The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other Nota de contenido: Theoretical Concepts -- Univariate Analysis of Stationary Time Series -- Multivariate Analysis of Stationary Time Series -- Non-stationary Time Series -- Cointegration -- Unit Root Tests -- Testing for the Order of Integration -- Further Considerations -- Cointegration -- Single-Equation Methods -- Multiple-Equation Methods En línea: http://dx.doi.org/10.1007/978-0-387-75967-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34217 Analysis of Integrated and Cointegrated Time Series with R [documento electrónico] / Pfaff, Bernhard ; SpringerLink (Online service) . - New York, NY : Springer New York, 2008 . - XX, 190 p : online resource. - (Use R!, ISSN 2197-5736) .
ISBN : 978-0-387-75967-8
Idioma : Inglés (eng)
Palabras clave: Mathematical statistics Probabilities Statistics Econometrics Economics Statistical Theory and Methods Probability Stochastic Processes in Computer Science Clasificación: 51 Matemáticas Resumen: The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics. This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R. The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models. The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes. The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other Nota de contenido: Theoretical Concepts -- Univariate Analysis of Stationary Time Series -- Multivariate Analysis of Stationary Time Series -- Non-stationary Time Series -- Cointegration -- Unit Root Tests -- Testing for the Order of Integration -- Further Considerations -- Cointegration -- Single-Equation Methods -- Multiple-Equation Methods En línea: http://dx.doi.org/10.1007/978-0-387-75967-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34217 Ejemplares
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Título : Analysis of Phylogenetics and Evolution with R Tipo de documento: documento electrónico Autores: Emmanuel Paradis ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2012 Colección: Use R!, ISSN 2197-5736 Número de páginas: XIV, 386 p. 89 illus Il.: online resource ISBN/ISSN/DL: 978-1-4614-1743-9 Idioma : Inglés (eng) Palabras clave: Life sciences Bioinformatics Evolutionary biology Statistics Sciences Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. In the second edition, the book continues to integrate a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. The second edition is completely updated, covering the full gamut of R packages for this area that have been introduced to the market since its previous publication five years ago. There is also a new chapter on the simulation of evolutionary data. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters Nota de contenido: Introduction -- First Steps in R for Phylogeneticists -- Phylogenetic Data in R -- Plotting Phylogenies -- Phylogeny Estimation -- Analysis of Macroevolution with Phylogenies -- Simulating Phylogenies and Evolutionary Data -- Developing and Implementing Phylogenetic Methods in R -- Short Course on Regular Expressions En línea: http://dx.doi.org/10.1007/978-1-4614-1743-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32769 Analysis of Phylogenetics and Evolution with R [documento electrónico] / Emmanuel Paradis ; SpringerLink (Online service) . - New York, NY : Springer New York, 2012 . - XIV, 386 p. 89 illus : online resource. - (Use R!, ISSN 2197-5736) .
ISBN : 978-1-4614-1743-9
Idioma : Inglés (eng)
Palabras clave: Life sciences Bioinformatics Evolutionary biology Statistics Sciences Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. In the second edition, the book continues to integrate a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. The second edition is completely updated, covering the full gamut of R packages for this area that have been introduced to the market since its previous publication five years ago. There is also a new chapter on the simulation of evolutionary data. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters Nota de contenido: Introduction -- First Steps in R for Phylogeneticists -- Phylogenetic Data in R -- Plotting Phylogenies -- Phylogeny Estimation -- Analysis of Macroevolution with Phylogenies -- Simulating Phylogenies and Evolutionary Data -- Developing and Implementing Phylogenetic Methods in R -- Short Course on Regular Expressions En línea: http://dx.doi.org/10.1007/978-1-4614-1743-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32769 Ejemplares
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Título : Applied Spatial Data Analysis with R Tipo de documento: documento electrónico Autores: Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Use R!, ISSN 2197-5736 Número de páginas: XIV, 376 p Il.: online resource ISBN/ISSN/DL: 978-0-387-78171-6 Idioma : Inglés (eng) Palabras clave: Medicine Epidemiology Geography Ecology Econometrics Regional economics Spatial & Public Health Regional/Spatial Science Environmental Monitoring/Analysis Geography, general Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom Nota de contenido: Handling Spatial Data in R -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Customising Spatial Data Classes and Methods -- Analysing Spatial Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Areal Data and Spatial Autocorrelation -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-0-387-78171-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34246 Applied Spatial Data Analysis with R [documento electrónico] / Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio . - New York, NY : Springer New York, 2008 . - XIV, 376 p : online resource. - (Use R!, ISSN 2197-5736) .
ISBN : 978-0-387-78171-6
Idioma : Inglés (eng)
Palabras clave: Medicine Epidemiology Geography Ecology Econometrics Regional economics Spatial & Public Health Regional/Spatial Science Environmental Monitoring/Analysis Geography, general Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom Nota de contenido: Handling Spatial Data in R -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Customising Spatial Data Classes and Methods -- Analysing Spatial Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Areal Data and Spatial Autocorrelation -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-0-387-78171-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34246 Ejemplares
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Título : Applied Spatial Data Analysis with R Tipo de documento: documento electrónico Autores: Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Use R!, ISSN 2197-5736 num. 10 Número de páginas: XVIII, 405 p. 121 illus., 89 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4614-7618-4 Idioma : Inglés (eng) Palabras clave: Statistics Geography for Life Sciences, Medicine, Health Sciences Engineering, Physics, Computer Science, Chemistry and Earth Statistics, general Environmental Monitoring/Analysis Geography, Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R, Second Edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003 Nota de contenido: Preface 2nd edition -- Preface 1st edition -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Classes for spatio-temporal Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-1-4614-7618-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32360 Applied Spatial Data Analysis with R [documento electrónico] / Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio . - New York, NY : Springer New York : Imprint: Springer, 2013 . - XVIII, 405 p. 121 illus., 89 illus. in color : online resource. - (Use R!, ISSN 2197-5736; 10) .
ISBN : 978-1-4614-7618-4
Idioma : Inglés (eng)
Palabras clave: Statistics Geography for Life Sciences, Medicine, Health Sciences Engineering, Physics, Computer Science, Chemistry and Earth Statistics, general Environmental Monitoring/Analysis Geography, Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R, Second Edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003 Nota de contenido: Preface 2nd edition -- Preface 1st edition -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Classes for spatio-temporal Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-1-4614-7618-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32360 Ejemplares
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Título : Bayesian Computation with R Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Jim Albert Editorial: New York, NY : Springer New York Fecha de publicación: 2007 Colección: Use R!, ISSN 2197-5736 Número de páginas: X, 268 p Il.: online resource ISBN/ISSN/DL: 978-0-387-71385-4 Idioma : Inglés (eng) Palabras clave: Statistics Computer simulation mathematics Mathematics Visualization Mathematical optimization and Computing/Statistics Programs Simulation Modeling Computational Numerical Analysis Optimization Clasificación: 51 Matemáticas Resumen: There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab Nota de contenido: An Introduction to R -- to Bayesian Thinking -- Single-Parameter Models -- Multiparameter Models -- to Bayesian Computation -- Markov Chain Monte Carlo Methods -- Hierarchical Modeling -- Model Comparison -- Regression Models -- Gibbs Sampling -- Using R to Interface with WinBUGS En línea: http://dx.doi.org/10.1007/978-0-387-71385-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34524 Bayesian Computation with R [documento electrónico] / SpringerLink (Online service) ; Jim Albert . - New York, NY : Springer New York, 2007 . - X, 268 p : online resource. - (Use R!, ISSN 2197-5736) .
ISBN : 978-0-387-71385-4
Idioma : Inglés (eng)
Palabras clave: Statistics Computer simulation mathematics Mathematics Visualization Mathematical optimization and Computing/Statistics Programs Simulation Modeling Computational Numerical Analysis Optimization Clasificación: 51 Matemáticas Resumen: There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab Nota de contenido: An Introduction to R -- to Bayesian Thinking -- Single-Parameter Models -- Multiparameter Models -- to Bayesian Computation -- Markov Chain Monte Carlo Methods -- Hierarchical Modeling -- Model Comparison -- Regression Models -- Gibbs Sampling -- Using R to Interface with WinBUGS En línea: http://dx.doi.org/10.1007/978-0-387-71385-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34524 Ejemplares
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