#
R (Computer program language)
Resource Information
The concept ** R (Computer program language)** represents the subject, aboutness, idea or notion of resources found in **Boston University Libraries**.

The Resource
R (Computer program language)
Resource Information

The concept

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

- Authority link
- (uri) http://id.loc.gov/authorities/subjects/sh2002004407

## Context

Context of R (Computer program language)#### Subject of

No resources found

No enriched resources found

- A Primer in Biological Data Analysis and Visualization Using R
- A beginners guide to R programming
- A course in statistics with R
- A modern approach to regression with R
- Adaptive tests of significance using permutations of residuals with R and SAS
- Advanced analytics with R and Tableau : advanced visual analytical solutions for your business
- Advances in social science research using R
- An R AND S-PLUS companion to multivariate analysis
- An R companion to applied regression
- An R companion to linear statistical models
- An introduction to analysis of financial data with R
- An introduction to bootstrap methods with applications to R
- Analysis of integrated and cointegrated time series with R
- Analyzing health data in R for SAS users
- Analyzing linguistic data : a practical introduction to statistics using R
- 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
- Automated data collection with R : a practical guide to web scraping and text mining
- Basic data analysis for time series with R
- Bayesian computation with R
- Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
- Beginning R : the statistical programming language
- 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
- Biostatistical design and analysis using R : a practical guide
- Biostatistics and computer-based analysis of health data using R
- Business analytics and data mining with R
- Comparing groups : randomization and bootstrap methods using R
- Competing risks and multistate models with R
- Complex surveys : a guide to analysis using R
- Computational network analysis with R : applications in biology, medicine, and chemistry
- Computational statistics : an introduction to R
- Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language
- 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
- Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan
- Easy statistics for food science with R
- Epidemiology with R
- Financial risk modelling and portfolio optimization with R
- Foundational and applied statistics for biologists using R
- Functional data analysis with R and MATLAB
- Getting started with R : an introduction for biologists
- Getting started with R : an introduction for biologists
- Getting started with neural nets in R
- Growth curve analysis and visualization using R
- Guidebook to R graphics using Microsoft Windows
- Handbook of fitting statistical distributions with 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
- Hurricane climatology : a modern statistical guide using R
- Instant heat maps in R how-to : learn how to design heat maps in R to enhance your data analysis
- Introduction to R for quantitative finance
- Introduction to stochastic processes with R
- Introductory statistics : a conceptual approach using R
- Introductory statistics with R
- Learning data mining with R : develop key skills and techniques with R to create and customize data mining algorithms
- 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
- Migrating from R to Python for data analysis
- Mixed effects models and extensions in ecology with R
- Modern data science with R
- Modern industrial statistics : with applications in R, MINITAB and JMP
- Molecular data analysis using R
- Multiple comparisons using R
- Multivariate time series analysis : with R and financial applications
- Multivariate time series analysis : with R and financial applications
- 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
- Permutation tests for stochastic ordering and ANOVA : theory and applications with R
- Practical R for mass communication and journalism
- Practical graph mining with R
- Probability with R : an introduction with computer science applications
- Probability with applications in R
- Psychologie statistique avec R
- R and Python for oceanographers : a practical guide with applications
- R and data mining : examples and case studies
- R and data mining : examples and case studies
- R data visualization cookbook : over 80 recipes to analyze data and create stunning visualizations with R
- R for SAS and SPSS users
- R for data science : learn and explore the fundamentals of data science with R
- R for dummies
- 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 object-oriented programming : a practical guide to help you learn and understand the programming techniques necessary to exploit the full power of R
- R statistical application development by example beginner's guide
- Self-affine scaling sets in R2
- Software for data analysis : programming with R
- Spatial and spatio-temporal Bayesian models with R-INLA
- Spatial data analysis in ecology and agriculture using R
- Spatial modeling in GIS and R for earth and environmental sciences
- Statistical analysis of questionnaires : a unified approach based on R and Stata
- Statistical analysis with R : beginner's guide
- Statistical computing with R
- Statistical hypothesis testing with SAS and R
- Statistical methods for hospital monitoring with R
- Statistical methods for overdispersed count data
- Statistical modelling in R
- Statistics for library and information services : a primer for using open source R software for accessibility and visualization
- Statistics for linguistics with R : a practical introduction
- Statistiques en sciences sociales avec R
- The R book
- The R book
- The essential R reference
- The new statistics with R : an introduction for biologists
- Time series analysis : with applications in R
- Understanding and applying basic statistical methods using R
- Using R for data analysis in social sciences : a research project-oriented approach
- Wavelet methods in statistics with R

## Embed

### Settings

Select options that apply then copy and paste the RDF/HTML data fragment to include in your application

Embed this data in a secure (HTTPS) page:

Layout options:

Include data citation:

<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.bu.edu/resource/z_sx2echdLo/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.bu.edu/resource/z_sx2echdLo/">R (Computer program language)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.bu.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.bu.edu/">Boston University Libraries</a></span></span></span></span></div>

Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements

### Preview

## Cite Data - Experimental

### Data Citation of the Concept R (Computer program language)

Copy and paste the following RDF/HTML data fragment to cite this resource

`<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.bu.edu/resource/z_sx2echdLo/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.bu.edu/resource/z_sx2echdLo/">R (Computer program language)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.bu.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.bu.edu/">Boston University Libraries</a></span></span></span></span></div>`