#
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 **European University Institute Library**.

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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**European University Institute Library**.- Label
- R (Computer program language)

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- A data scientist's guide to acquiring, cleaning, and managing data in R
- A first course in statistical programming with R
- A guide to R for social and behavioral science statistics
- A modern approach to regression with R
- A survivor's guide to R : an introduction for the uninitiated and the unnerved
- A tour of data science : learn R and Python in parallel
- A user's guide to business analytics
- Advanced R
- Advanced R Solutions
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advanced analytics in Power BI with R and Python : ingesting, transforming, visualizing
- Advanced machine learning with R : tackle data analytics and machine learning challenges and build complex applications with R 3.5
- Advanced regression models with SAS and R
- Advanced statistics with applications in R
- An R companion for applied statistics II : multivariable and multivariate techniques
- An R companion for the third edition of the fundamentals of political science research
- An R companion to applied regression
- An introduction to analysis of financial data 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 : with 19 figures
- Analyzing social network using R
- Analyzing spatial models of choice and judgment with R
- Applied econometrics with R
- Applied mathematics with open-source software : operational research problems with Python and R
- Applied probabilistic calculus for financial engineering : an introduction using R
- Applied regularization methods for the social sciences
- Applied spatial data analysis with R
- Applied spatial statistics and econometrics : data analysis in R
- Applied statistics : theory and problem solutions with R
- Applied statistics using R : a guide for the social sciences
- Applied statistics using SPSS, STATISTICA, MATLAB and R
- Automated data collection with R : a practical guide to Web scraping and text mining
- Automated data collection with R : a practical guide to web scraping and text mining
- Bare-bones R : a brief introductory guide
- Basic data analysis for time series with R
- Basic statistics with R : reaching decisions with data
- Bayes rules! : an introduction to Bayesian modeling with R
- Bayesian methodology : an overview with the help of R software
- Bayesian modeling of spatio-temporal data with R
- Bayesian networks : with examples in R
- Beginning R 4 : from beginner to pro
- Beyond spreadsheets with R : a beginner's guide to R and RStudio
- Business case analysis with R : simulation tutorials to support complex business decisions
- Business statistics with solutions in R
- Categorical data analysis and multilevel modeling using R
- Complex surveys : a guide to analysis using R
- Computational methods for numerical analysis with R
- Conceptual econometrics using R
- Correspondence analysis and data coding with Java and R
- Data analysis and graphics using R : an example-based approach
- Data analysis using hierarchical generalized linear models with R
- Data analytics : a small data approach
- Data analytics for the social sciences : applications in R
- Data management in R : a guide for social scientists
- Data manipulation With R
- Data mining algorithms : explained using R
- Data mining and business analytics with R
- Data mining applications with R
- Data mining for business analytics : concepts, techniques, and applications in R
- Data visualization : a practical introduction
- Data visualization for social and policy research : a step-by-step approach using R and Python
- Deep learning with R
- Design and analysis of experiments with R
- Discovering statistics using R
- Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan
- Doing data science in R : an introduction for social scientists
- Dynamic linear models with R
- Easy R : access, prepare, visualize, explore data, and write papers
- Ensemble classification methods with applications in R
- Event history analysis with R
- Exploratory data analysis using R
- Exploratory multivariate analysis by example using R
- Exploring everyday things with R and Ruby
- Financial analytics with R : building a laptop laboratory for data science
- Financial risk modelling and portfolio optimization with R
- Financial, macro and micro econometrics using R
- Flexible regression and smoothing : using GAMLSS in R
- Foundations and applications of statistics : an introduction using R
- Foundations of statistics for data scientists : with R and Python
- Functional data structures in R : advanced statistical programming in R
- Fundamentals of causal inference : with R
- Generalized additive models : an introduction with R
- Generalized additive models : an introduction with R
- Graphical data analysis with R
- Handbook of fitting statistical distributions with R
- Handbook of regression analysis with applications in R
- Handbook of regression modeling in people analytics : with examples in R and Python
- Hands-on intermediate econometrics using R : templates for extending dozens of practical examples
- Hands-on machine learning with R
- Hidden markov models for time series : an introduction using R
- IFRS 9 and CECL credit risk modelling and validation : a practical guide with examples worked in R and SAS
- Instant R : an introduction to R for statistical analysis
- Integrated population models : theory and ecological applications with R and JAGS
- 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 social scientists : a tidy programming approach
- Introduction to data science : data analysis and prediction algorithms with R
- Introduction to data science for social and policy research : collecting and organizing data with R and Python
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
- Introduction to machine learning with R : rigorous mathematical analysis
- Introductory statistics : a problem-solving approach
- Introductory statistics with R
- Just Enough R! : An Interactive Approach to Machine Learning and Analytics
- Learn R for applied statistics : with data visualizations, regressions, and statistics
- Learning data mining with R : develop key skills and techniques with R to create and customize data mining algorithms
- Learning microeconometrics with R
- Learning statistics using R
- Linear models and regression with R : an integrated approach
- Linear regression analysis with JMP and R
- Linear regression models : applications in R
- Machine learning with R : discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R
- Making your case : using R for program evaluation
- Mastering data analysis with R : gain clear insights into your data and solve real-world data science problems with R, from data munging to modeling and visualization
- Mastering parallel programming with R : master the robust features of R parallel programming to accelerate your data science computations / Simon R. Chapple, Eilidh Troup, Thorsten Forster, Terence Sloan
- Mastering predictive analytics with R : master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
- Mathematical Foundations of Data Science Using R
- Mathematical statistics with applications in R
- Mathematical statistics with resampling and R
- Mathematics and programming for machine learning with R : from the ground up
- Modeling techniques in predictive analytics : business problems and solutions with R
- Modern data science with R
- Moving from IBM SPSS to R and RStudio : a statistics companion
- Multilevel modeling using R
- Multilevel modeling using R
- Multilevel modeling using R
- Multivariate time series analysis : with R and financial applications
- Nonlinear parameter optimization using R tools
- Nonlinear regression with R
- Nonparametric hypothesis testing : rank and permutation methods with applications in R
- Numerical analysis using R : solutions to ODEs and PDEs
- Panel data econometrics with R
- Permutation tests for stochastic ordering and ANOVA : theory and applications with R
- Practical Machine Learning in R
- Practical R 4 : applying R to data manipulation, processing and integration
- Practical data science cookbook : 89 hands-on recipes to help you complete real-world data science projects in R and Python
- Practical data science with R
- Practical propensity score methods using R
- Practical time series analysis : prediction with statistics and machine learning
- Predictive analytics : parametric models for regression and classification using R
- Probability and mathematical statistics : theory, applications, and practice in R
- Probability and statistics with R
- Probability, statistics, and data : a fresh approach using R
- Python and R for the modern data scientist : the best of both worlds
- Python for R users : a data science approach
- Qualitative comparative analysis (QCA) using R : A Beginner's Guide
- Qualitative comparative analysis with R : a user's guide
- Quantitative methods in archaeology using R
- R Markdown : the definitive guide
- R companion for sampling : design and analysis
- R companion to elementary applied statistics
- R cookbook : proven recipes for data analysis, statistics, and graphics
- R for Microsoft Excel users : making the transition for statistical analysis
- R for college mathematics and statistics
- R for data science : import, tidy, transform, visualize, and model data
- R for stata users
- R graph essentials : use R's powerful graphing capabilities to design and create professional-level graphics
- R graphics
- R graphics cookbook
- R high performance programming : overcome performance difficulties in R with a range of exciting techniques and solutions
- R in a nutshell
- R in action : data analysis and graphics with R
- R in finance and economics : a beginner's guide
- R markdown cookbook
- R quick syntax reference : a pocket guide to the language, APIs and library
- Reasoning with data : an introduction to traditional and Bayesian statistics using R
- Reproducible research with R and RStudio
- Reproducible research with R and RStudio
- Robust nonlinear regression : with applications using R
- Robust statistical methods with R
- SAS for R users : a book for budding data scientists
- Spatial econometric methods in agricultural economics using R
- Spatio-temporal statistics with R
- Statistical analysis of financial data : with examples in R
- Statistical analysis of questionnaires : a unified approach based on R and Stata
- Statistical computing with R
- Statistical data cleaning with applications in R
- Statistical inference via data science : a ModernDive, into R and the tidyverse
- Statistical methods for mediation, confounding and moderation analysis using R and SAS
- Statistical modelling in R
- 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 Using R
- Statistics for psychology using R
- Statistics using R
- Statistics with R : a beginner's guide
- Stochastic processes with R : an introduction
- Sufficient dimension reduction : methods and applications with R
- Text mining with R : a tidy approach
- Texts in statistical science : graphics for statistics and data analysis with R
- The R book
- The R software : fundamentals of programming and statistical analysis
- The art of R programming : a tour of statistical software design
- The big R-book : from data science to learning machines for the professional
- The essentials of data science : knowledge discovery using R
- Time series : a data analysis approach using R
- Time series analysis : with applications in R
- Uncertainty analysis of experimental data with R
- Understanding statistics using R
- Using R for data analysis in social sciences : a research project-oriented approach
- Using R for introductory statistics
- Using R for item response theory model applications
- Using Shiny to teach econometric models
- Using the R commander : a point-and-click interface for 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.library.eui.eu/resource/nDejP-oRiaw/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/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.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="https://link.library.eui.eu/">European University Institute Library</a></span></span></span></span></div>`