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R (Computer program language)
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The concept ** R (Computer program language)** represents the subject, aboutness, idea or notion of resources found in **University of Liverpool**.

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R (Computer program language)
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**R (Computer program language)**represents the subject, aboutness, idea or notion of resources found in**University of Liverpool**.- Label
- R (Computer program language)

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- A beginner's guide to R
- A beginner's guide to data exploration and visualisation with R
- A beginner's guide to generalised additive mixed models with R
- A course in statistics with R
- A course in statistics with R
- A first course in statistical programming with R
- A first course in statistical programming with R
- A modern approach to regression with R
- A practical guide to ecological modelling : using R as a simulation platform
- A survivor's guide to R : an introduction for the uninitiated and the unnerved
- A user's guide to network analysis in R
- Adaptive tests of significance using permutations of residuals with R and SAS
- Advanced Object-Oriented Programming in R : Statistical Programming for Data Science, Analysis and Finance
- Advanced R
- Advanced R : data programming and the cloud
- Advanced analytics with R and Tableau : advanced visual analytical solutions for your business
- Advanced analytics with R and Tableau : advanced visual analytical solutions for your business
- An R AND S-plus companion to multivariate analysis
- An R and S-PLUSÂ® companion to multivariate analysis
- An R companion to applied regression
- An introduction to R : notes on R : a programming environment for data analysis and graphics, version 1.9.1
- An introduction to R : notes on R: a programming environment for data analysis and graphics, version 1.4.1
- An introduction to R for spatial analysis & mapping
- An introduction to R for spatial analysis & mapping
- An introduction to analysis of financial data with R
- An introduction to applied multivariate analysis with R
- An introduction to bootstrap methods with applications to R
- An introduction to data analysis using aggregation functions in R
- An introduction to statistical inference and its applications with R
- An introduction to statistical learning : with applications in R
- An introduction to the advanced theory of nonparametric econometrics : a replicable approach using R
- Analysis of integrated and cointegrated time series with R
- Analysis of phylogenetics and evolution with R
- Analysis of phylogenetics and evolution with R
- Analyzing compositional data with R
- Analyzing linguistic data : a practical introduction to statistics using R
- Applied Statistics for Environmental Science with R
- Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
- Applied econometrics with R
- Applied hierarchical modeling in ecology : analysis of distribution, abundance and species richness in R and BUGS, Volume 1, Prelude and static models
- Applied hierarchical modeling in ecology : analysis of distribution, abundance and species richness in R and BUGS, Volume 1, Prelude and static models
- Applied probabilistic calculus for financial engineering : an introduction using R
- Applied spatial data analysis with R
- Applied spatial data analysis with R
- Applied spatial data analysis with R
- Applied spatial data analysis with R
- Applied statistical genetics with R : for population-based association studies
- Applying Test Equating Methods : using R
- Automated data collection with R : a practical guide to web scraping and text mining
- Automated trading with R : quantitative research and platform development
- Bare-bones R : a brief introductory guide
- Basic data analysis for time series with R
- Basic elements of computational statistics
- Basic statistics : an introduction with R
- Bayesian computation with R
- Bayesian cost-effectiveness analysis with the R package BCEA
- Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
- Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
- Bayesian networks in R : with applications in systems biology
- Beginner's guide to spatial, temporal, and spatial-temporal ecological data analysis with R-INLA , Volume I, Using GLM and GLMM
- Beginning R : an introduction to statistical programming
- Beginning R : an introduction to statistical programming
- Beginning R : the statistical programming language
- Beginning data science with R
- Behavioral research data analysis with R
- Big Data Analytics with R
- Big data analytics with R : utilize R to uncover hidden patterns in your big data
- Big data analytics with R and Hadoop
- Bioconductor case studies
- Bioinformatics and computational biology solutions using R and Bioconductor
- Bioinformatics with R cookbook : over 90 practical recipes for computational biologists to model and handle real-life data using R
- Biostatistical design and analysis using R : a practical guide
- Biostatistics and computer-based analysis of health data using R
- Biostatistics with R : an introduction to statistics through biological data
- Business analytics and data mining with R
- Business analytics using R - a practical approach
- Business case analysis with R : simulation tutorials to support complex business decisions
- Chemometrics with R : multivariate data analysis in the natural sciences and life Sciences
- Competing risks and multistate models with R
- Complex surveys : a guide to analysis using R
- Computational finance : an introductory course with R
- Computational network analysis with R : applications in biology, medicine, and chemistry
- Computer simulation and data analysis in molecular biology and biophysics : an introduction using R
- Contingency table analysis : methods and implementation using R
- Corpus linguistics and statistics with R : introduction to quantitative methods in linguistics
- Data analysis and graphics using R : an example-based approach
- Data analysis and graphics using R : an example-based approach
- Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language
- Data manipulation With R
- Data manipulation with R : efficiently perform data manipulation using the split-apply-combine strategy in R
- Data manipulation with R : perform group-wise data manipulation and deal with large datasets using R efficiently and effectively
- Data mining algorithms : explained using R
- Data mining applications with R
- Data mining applications with R
- Data mining with Rattle and R : the art of excavating data for knowledge discovery
- Data science and predictive analytics : biomedical and health applications using R
- Data visualisation with R : 100 examples
- Datenvisualisierung mit R : 111 Beispiele
- Discovering statistics using R
- Distributions for modelling location, scale, and shape : using GAMLSS in R
- Doing Bayesian data analysis : a tutorial with R and BUGS
- Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan
- Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan
- Doubly classified model with R
- Dynamic documents with R and knitr
- Dynamic linear models with R
- Easy statistics for food science with R
- Easy statistics for food science with R
- Ecological models and data in R
- Elements of Copula Modeling with R
- Empirical likelihood method in survival analysis
- EnvStats : an R package for environmental statistics
- Epidemics : models and data using R
- Examples in parametric inference with R
- Exploratory multivariate analysis by example using R
- Feature Engineering and Selection : a Practical Approach for Predictive Models
- Financial analytics with R : building a laptop laboratory for data science
- Financial risk modelling and portfolio optimization with R
- Flexible regression and smoothing : using GAMLSS in R
- Forest analytics with R : an introduction
- Functional data analysis with R and MATLAB
- Functional data structures in R : advanced statistical programming in R
- Functional programming in R : advanced statistical programming for data science, analysis and finance
- Generalized additive models : an introduction with R
- Generalized linear models with examples in R
- Geochemical modelling of igneous processes-- principles and recipes in R language : bringing the power of R to a geochemical community
- Getting started with R : an introduction for biologists
- Getting started with R : an introduction for biologists
- Getting started with R : an introduction for biologists
- Getting started with R : an introduction for biologists
- Ggplot2 : elegant graphics for data analysis
- Ggplot2 : elegrant graphics for data analysis
- Graphical models with Rh[electronic book]
- Guide to programming and algorithms using R
- Guidebook to R graphics using Microsoft Windows
- Habitat suitability and distribution models : with applications in R
- Handbook of fitting statistical distributions with R
- Handbook of statistics : computational statistics with R
- Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques
- Hidden Markov models for time series : an introduction using R
- Humanities data in R : exploring networks, geospatial data, images, and text
- Hurricane climatology : a modern statistical guide using R
- IFRS 9 and CECL credit risk modelling and validation : a practical guide with examples worked in R and SAS
- Instant R starter : jump into the R programming language and go beyond "Hello World!"
- Instant heat maps in R how-to : learn how to design heat maps in R to enhance your data analysis
- Interactive and dynamic graphics for data analysis : with R and Ggobi
- Introducing Monte Carlo methods with R
- Introducing data science for social and policy research : collecting and organizing data with R and Python
- Introduction to R for quantitative finance
- Introduction to R for terrestrial ecology : basics of numerical analysis, mapping, statistical tests and advanced application of R
- Introduction to data analysis with R for forensic scientists
- Introduction to image processing using R : learning by examples
- Introduction to nonparametric statistics for the biological sciences using R
- Introduction to probability simulation and Gibbs sampling with R
- Introduction to statistics and data analysis : with exercises, solutions and applications in R
- Introduction to stochastic processes with R
- Introductory adaptive trial designs : a practical guide with R
- Introductory statistics : a conceptual approach using R
- Introductory statistics with R
- Introductory time series with R
- Joint models for longitudinal and time-to-event data : with applications in R
- Joint species distribution modelling : with applications in R
- Lattice : multivariate data visualization with R
- Learn R for applied statistics : with data visualizations, regressions, and statistics
- Learn business analytics in six steps using SAS and R : a practical, step-by-step guide to learning business analytics
- Learn ggplot2 using shiny app
- Learning R Programming
- Learning analytics in R with SNA, LSA, and MPIA
- Learning data mining with R : develop key skills and techniques with R to create and customize data mining algorithms
- Learning statistics using R
- Linear mixed-effects models using R : a step-by-step approach
- Linear models with R
- Machine Learning Using R : With Time Series and Industry-Based Use Cases in R
- Machine learning using R
- Machine learning with R
- Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications
- Making your case : using R for program evaluation
- Mastering scientific computing with R : employ professional quantitative methods to answer scientific questioins with a powerful open source data analysis environment
- Mathematical statistics with applications in R
- Mathematical statistics with applications in R
- Meta-analysis with R
- Metaprogramming in R : advanced statistical programming for data science, analysis, and finance
- Mixed effects models and extensions in ecology with R
- Model-based clustering and classification for data science : with applications in R
- Modeling psychophysical data in R
- Modern industrial statistics : with applications in R, MINITAB and JMP
- Modern optimization with R
- Modern psychometrics with R
- Moderne Datenanalyse mit R : Daten einlesen, aufbereiten, visualisieren, modellieren und kommunizieren
- Molecular data analysis using R
- Morphometrics with R
- Multiple comparisons using R
- Multiple decrement models in Insurance : an introduction using R
- Multistate analysis of life histories with R
- Multivariate methods of representing relations in R for prioritization purposes : selective scaling, comparative clustering, collective criteria and sequenced sets
- Multivariate nonparametric methods with R : an approach based on spatial signs and ranks
- Multivariate statistical quality control using R
- Multivariate time series analysis : with R and financial applications
- Nonlinear parameter optimization using R tools
- Nonlinear regression with R
- Nonlinear time series analysis with R
- Nonparametric hypothesis testing : rank and permutation methods with applications in R
- Nonparametric statistical methods using R
- Numerical analysis using R : solutions to ODEs and PDEs
- Numerical analysis using R : solutions to ODEs and PDEs
- Numerical ecology with R
- Numerical ecology with R
- Numerical integration of space fractional partial differential equations, Vol 1, Introduction to algorithms and computer coding in R
- Oceanographic analysis with R
- Permutation tests for stochastic ordering and ANOVA : theory and applications with R
- Political analysis using R
- Practical data science with R
- Practical graph mining with R
- Practical guide to cluster analysis in R : unsupervised machine learning
- Pro data visualization using R and JavaScript
- Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R
- Probability and statistics with R
- Probability with R : an introduction with computer science applications
- Probability with applications in R
- Production and efficiency analysis with R
- Programmieren mit R
- Programming graphical user interfaces in R
- Psychologie statistique avec R
- QCA with R : a comprehensive resource
- Qualitative comparative analysis with R : a user's guide
- Quality Control with R: An ISO Standards Approach
- Quantitative methods in archaeology using R
- Quantitative methods in archaeology using R
- Quantitative social science data with R : an introduction
- R Markdown : The Definitive Guide
- R and MATLAB
- R and MATLAB
- R and Python for oceanographers : a practical guide with applications
- R and data mining : examples and case studies
- R by example : concepts to code
- R cookbook
- R cookbook
- R data visualization cookbook : over 80 recipes to analyze data and create stunning visualizations with R
- R for Everyone : Advanced Analytics and Graphics
- R for SAS and SPSS users
- R for SAS and SPSS users
- R for Stata users
- R for business analytics
- R for cloud computing : an approach for data scientists
- R for data science : import, tidy, transform, visualize, and model data
- R for data science : learn and explore the fundamentals of data science with R
- R for dummies
- R for finite element analyses of size-dependent microscale structures
- R for marketing research and analytics
- R graphics
- R graphs cookbook : detailed hands-on recipes for creating the most useful types of graphs in R-- starting from the simplest versions to more advanced applications
- R graphs cookbook : over 70 recipes for building and customizing publication-quality visualization of powerful and stunning R graphs
- R high performance programming : overcome performance difficulties in R with a range of exciting techniques and solutions
- R in action : data analysis and graphics with R
- R in action : data analysis and graphics with R
- R object-oriented programming : a practical guide to help you learn and understand the programming techniques necessary to exploit the full power of R
- R packages
- R quick syntax reference
- R statistical application development by example beginner's guide
- R through Excel : a spreadsheet interface for statistics, data analysis, and graphics
- Seamless R and C++ integration with Rcpp
- Semiparametric regression with R
- Simulation and inference for stochastic processes with YUIMA : a comprehensive R framework for SDEs and other stochastic processes
- Singular Spectrum Analysis : Using R
- Six sigma with R : statistical engineering for process improvement
- Software for data analysis : programming with R
- Solving differential equations in R
- Spatial and spatio-temporal Bayesian models with R-INLA
- Spatial data analysis in ecology and agriculture using R
- Spatial ecology and conservation modeling : applications with R
- Spatial modeling in GIS and R for earth and environmental sciences
- Statistical analysis and data display : an intermediate course with examples in R
- Statistical analysis and data display : an intermediate course with examples in S-plus, R, and SAS
- Statistical analysis of financial data in R
- Statistical analysis of network data with R
- Statistical analysis with R : beginner's guide
- Statistical analysis with R for dummies
- Statistical disclosure control for microdata : methods and applications in R
- Statistical hypothesis testing with SAS and R
- Statistical methods for environmental epidemiology with R : a case study in air pollution and health
- Statistical methods for hospital monitoring with R
- Statistical methods for overdispersed count data
- Statistical rethinking : a Bayesian course with examples in R and Stan
- Statistical rethinking : a bayesian course with examples in R and stan
- Statistics : an introduction using R
- Statistics and data analysis for microarrays using R and Bioconductor
- Statistics and data analysis for microarrays using R and Bioconductor
- Statistics for ecologists using R and Excel : data collection, exploration, analysis and presentation
- Statistics for ecologists using R and Excel : data collection, exploration, analysis and presentation
- Statistics for linguistics with R : a practical introduction
- Statistics for linguistics with R : a practical introduction
- Statistics for linguists : an introduction using R
- Statistics for psychology using R
- Statistiques en sciences sociales avec R
- System dynamics modeling with R
- Text analysis with R for students of literature
- The R book
- The R software : fundamentals of programming and statistical analysis
- The art of R programming : a tour of statistical software design
- The art of R programming : tour of statistical software design
- The basics of item response theory using R
- The essential R reference
- The new statistics with R : an introduction for biologists
- Time Series Analysis and Its Applications : With R Examples
- Time series analysis : with applications in R
- Time series analysis : with applications in R
- Two-way analysis of variance : statistical tests and graphics using R
- Understanding and applying basic statistical methods using R
- Understanding statistics using R
- Using R for data analysis in social sciences : a research project-oriented approach
- Using R for digital soil mapping
- Using R for introductory statistics
- Using R for introductory statistics
- Using R for statistics
- Wavelet methods in statistics with R
- Working with the American community survey in R : a guide to using the acs package
- Zero inflated models and generalized linear mixed models with R
- ggplot2 : elegant graphics for data analysis

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`<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/resource/nDejP-oRiaw/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/resource/nDejP-oRiaw/">R (Computer program language)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">University of Liverpool</a></span></span></span></span></div>`