Gentleman, Robert 1959
Overview
Works:  17 works in 77 publications in 2 languages and 1,686 library holdings 

Roles:  Editor, Author 
Publication Timeline
.
Most widely held works by
Robert Gentleman
Bioinformatics and computational biology solutions using R and Bioconductor by
Robert Gentleman(
)
27 editions published between 2005 and 2012 in English and Japanese and held by 1,013 WorldCat member libraries worldwide
"This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers."Jacket
27 editions published between 2005 and 2012 in English and Japanese and held by 1,013 WorldCat member libraries worldwide
"This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers."Jacket
R programming for bioinformatics by
Robert Gentleman(
Book
)
21 editions published between 2008 and 2009 in English and held by 449 WorldCat member libraries worldwide
"Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems." "Drawing on the author's firsthand experiences as an expert in R, the book begins with coverage of the general properties of the R language, several unique programming aspects of R, and objectoriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code." "With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology."Jacket
21 editions published between 2008 and 2009 in English and held by 449 WorldCat member libraries worldwide
"Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems." "Drawing on the author's firsthand experiences as an expert in R, the book begins with coverage of the general properties of the R language, several unique programming aspects of R, and objectoriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code." "With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology."Jacket
Applied spatial data analysis with R by
Roger Bivand(
)
1 edition published in 2008 in English and held by 83 WorldCat member libraries worldwide
This book addresses the needs of researchers and students using R to analyze spatial data across a range of disciplines and professions. The book is coauthored by a group involved in the Comprehensive R Archive Network
1 edition published in 2008 in English and held by 83 WorldCat member libraries worldwide
This book addresses the needs of researchers and students using R to analyze spatial data across a range of disciplines and professions. The book is coauthored by a group involved in the Comprehensive R Archive Network
Nonlinear regression with R by
Christian Ritz(
)
2 editions published in 2009 in English and held by 70 WorldCat member libraries worldwide
Statistics, data analysis, computing
2 editions published in 2009 in English and held by 70 WorldCat member libraries worldwide
Statistics, data analysis, computing
Bioconductor case studies by
Florian Hahne(
)
10 editions published between 2008 and 2010 in English and Undetermined and held by 29 WorldCat member libraries worldwide
Bioconductor software has become a standard tool for the analysis and comprehension of data from highthroughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning f
10 editions published between 2008 and 2010 in English and Undetermined and held by 29 WorldCat member libraries worldwide
Bioconductor software has become a standard tool for the analysis and comprehension of data from highthroughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning f
Bioinformatics with R by
Robert Gentleman(
Book
)
2 editions published in 2008 in English and held by 16 WorldCat member libraries worldwide
2 editions published in 2008 in English and held by 16 WorldCat member libraries worldwide
Analysis of Integrated and Cointegrated Time Series with R by
Bernhard Pfaff(
)
2 editions published in 2008 in English and held by 11 WorldCat member libraries worldwide
2 editions published in 2008 in English and held by 11 WorldCat member libraries worldwide
Bioinformatics and Computational Biology Solutions Using R and Bioconductor by
Robert Gentleman(
)
1 edition published in 2005 in English and held by 3 WorldCat member libraries worldwide
1 edition published in 2005 in English and held by 3 WorldCat member libraries worldwide
R : a language for data analysis and graphics by Ross Ihaka(
Book
)
1 edition published in 1994 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 1994 in English and held by 2 WorldCat member libraries worldwide
Exploratory methods for censored data by
Robert Gentleman(
Book
)
2 editions published in 1988 in English and held by 2 WorldCat member libraries worldwide
2 editions published in 1988 in English and held by 2 WorldCat member libraries worldwide
Nonlinear covariates in the proportional hazards model by
Robert Gentleman(
Book
)
1 edition published in 1988 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 1988 in English and held by 2 WorldCat member libraries worldwide
Special issue on multivariate methods in genomic data analysis(
Book
)
1 edition published in 2004 in English and held by 1 WorldCat member library worldwide
1 edition published in 2004 in English and held by 1 WorldCat member library worldwide
An introduction to statistical computing with R by
Robert Gentleman(
Book
)
1 edition published in 1995 in English and held by 1 WorldCat member library worldwide
1 edition published in 1995 in English and held by 1 WorldCat member library worldwide
Statistics for Biology and Health : Bioinformatics and Computational Biology Solutions Using R and Bioconductor(
)
1 edition published in 2005 in English and held by 1 WorldCat member library worldwide
Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from highthroughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of highthroughput data from microarray, proteomic, and flow cytometry platforms; curation and delivery of biological metadata for use in statistical modeling and interpretation; statistical analysis of highthroughput data, including machine learning and visualization; modeling and visualization of graphs and networks; and the developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document.; Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers
1 edition published in 2005 in English and held by 1 WorldCat member library worldwide
Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from highthroughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of highthroughput data from microarray, proteomic, and flow cytometry platforms; curation and delivery of biological metadata for use in statistical modeling and interpretation; statistical analysis of highthroughput data, including machine learning and visualization; modeling and visualization of graphs and networks; and the developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document.; Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers
Bayesian Computation with R by
Robert Gentleman(
Book
)
1 edition published in 2007 in English and held by 1 WorldCat member library worldwide
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 simulationbased 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 twoparameter 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 simulationbased algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, orderrestricted 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
1 edition published in 2007 in English and held by 1 WorldCat member library worldwide
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 simulationbased 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 twoparameter 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 simulationbased algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, orderrestricted 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
Bioconductor: open software development for computational biology and bioinformatics(
Book
)
2 editions published in 2004 in English and held by 0 WorldCat member libraries worldwide
2 editions published in 2004 in English and held by 0 WorldCat member libraries worldwide
more
fewer
Audience Level
0 

1  
Kids  General  Special 
Related Identities
Useful Links
Associated Subjects
Animal genetics Bayesian statistical decision theoryData processing Bioconductor (Computer file) Bioinformatics Biomathematics Cartography Computational intelligence Computer science Distribution (Probability theory) Ecology Econometrics Engineering Epidemiology Estimation theory Forests and forestry Mathematical statistics Mathematical statisticsData processing Mathematics Model theory Nonlinear theories R (Computer program language) Regression analysisData processing Spatial analysis (Statistics)Data processing Statistics StatisticsGraphic methods Toxicology