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- 25 Recipes For Getting Started With R
- A Beginner's Guide to R
- A Concise Handbook of Business Research. Special Emphasis on Data Analysis Using Excel and R
- A Course in Statistics With R
- A Data Scientists Guide to Acquiring, Cleaning, and Managing Data in R
- A Discussion Of Time Series Objects For R In Finance
- A First Course in Statistical Programming with R (3rd)
- A handbook of statistical analyses using R (2nd)
- A Modern Approach To Regression With R
- A Practical Guide to Ecological Modelling. Using R as a Simulation Platform
- A Primer for Spatial Econometrics. With Applications in R
- A Primer in Biological Data Analysis and Visualization Using R (2nd)
- A Primer Of Ecology With R
- A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio
- A Step-By-Step R Tutorial
- A Tiny Handbook Of R
- A Tour Of Data Science. Learn R And Python In Parallel
- A User’s Guide to Network Analysis in R
- Adaptive Design Theory and Implementation using SAS and R
- Advanced Analytics in Power BI with R and Python
- Advanced Environmental Monitoring with Remote Sensing Time Series Data and R
- Advanced Object-Oriented Programming in R
- Advanced R (2nd)
- Advanced R 4. Data Programming and the Cloud (2nd)
- Advanced R Solutions
- Advanced R Statistical Programming and Data Models. Analysis, and Machine Learning,
- Advanced R. Data Programming and the Cloud
- Advanced Regression Models With Sas and R
- Advanced spatial modeling with stochastic partial differential equations using R and INLA
- Advanced Statistics for the Behavioral Sciences. A Computational Approach with R
- Advanced Statistics With Applications in R
- Advances In Social Science Research Using R
- Advancing into Analytics. From Excel to Python and R
- 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 Clustering with R
- An introduction to data cleaning with R
- An Introduction To R
- An Introduction to R and Python for Data Analysis
- An Introduction to R for Quantitative Economics
- An Introduction to R for Spatial Analysis and Mapping, Second edition
- An Introduction to Spatial Data Analysis in R
- An Introduction to Statistical Inference and Its Applications with R
- An Introduction to Statistical Learning. With Applications in R (2nd)
- An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics. A Replicable Approach Using R
- An Introduction to the Rasch Model With Examples in R
- An R And S-Plus Companion To Applied Regression
- An R And S-Plus Companion To Multivariate Analysis
- An R Companion to Applied Regression (3rd)
- An R Companion To Linear Statistical Models
- Analysing spatial point patterns in R
- Analysis of categorical data with R
- Analysis of Correlated Data With Sas and R
- Analysis of Integrated and Cointegrated Time Series with R
- Analysis Of Phylogenetics And Evolution With R
- Analysis of questionnaire data with R
- Analyzing Baseball Data With R
- Analyzing Compositional Data With R
- Analyzing Financial Data and Implementing Financial Models Using R (2nd)
- Analyzing Health Data in R for Sas Users
- Analyzing Linguistic Data. A Practical Introduction To Statistics Using R
- Analyzing Sensory Data With R
- Analyzing Social Networks Using R
- Analyzing Spatial Models of Choice and Judgment with R
- Analyzing US Census Data. Methods, Maps, and Models in R
- ANOVA and Mixed Models. A Short Introduction Using R
- Applications of Regression for Categorical Outcomes Using R
- Applied Analytics Through Case Studies Using Sas and R
- Applied Bayesian Statistics. With R and OpenBUGS Examples
- Applied Biclustering Methods For Big And High Dimensional Data Using R
- Applied Calculus with R
- Applied Compositional Data Analysis With Worked Examples in R
- Applied Econometrics With R
- Applied Linear Regression for Business Analytics with R
- Applied Mathematics with Open-Source Software. Operational Research Problems with Python and R
- Applied Meta-Analysis With R
- Applied Meta-Analysis with R and Stata (2nd)
- Applied Multivariate Statistical Analysis and Related Topics with R
- Applied Multivariate Statistics with R (2nd)
- Applied Probabilistic Calculus for Financial Engineering. An Introduction Using R
- Applied Spatial Data Analysis With R (2nd)
- Applied Spatial Statistics and Econometrics. Data Analysis in R
- Applied Statistical Genetics With R
- Applied Statistics for Environmental Science With R
- Applied Statistics Using Spss, Statistica, Matlab And R (2nd)
- Applied Statistics. Theory and Problem Solutions With R
- Applied Supervised Learning with R
- Applied Survival Analysis Using R
- Applied Time Series Analysis With R
- Applied Univariate, Bivariate, and Multivariate Statistics. With Applications in SPSS and R (2nd)
- Applying Test Equating Methods Using R
- Asymptotic Statistical Inference. A Basic Course Using R
- Automated Data Collection with R. A Practical Guide to Web Scraping and Text Mining
- Automated Trading With R
- Basic Data Analysis for Time Series With R
- Basic R for Finance
- Basic Statistics for the Behavioral and Social Sciences Using R
- Basic Statistics. An Introduction With R
- Basics Of Matrix Algebra For Statistics With R
- Basketball Data Science With Applications in R
- Bayes Factors for Forensic Decision Analyses with R
- Bayes Rule with R. A Tutorial Introduction to Bayesian Analysis
- Bayesian Analysis with R for Drug Development. Concepts, Algorithms, and Case Studies
- Bayesian Applications in Environmental and Ecological Studies with R and Stan
- Bayesian Computation With R
- Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan
- Bayesian Essentials With R (2nd)
- Bayesian Hierarchical Models With Applications Using R (2nd)
- Bayesian Modelling of Spatio-Temporal Data with R
- Bayesian Networks in R
- Bayesian Networks. With Examples in R (2nd)
- Bayesian Statistical Modeling With Stan, R, and Python
- Beginning Data Science in R
- Beginning Data Science in R 4 (2nd)
- Beginning Data Science With R
- Beginning R (2nd)
- Beginning R 4. From Beginner to Pro
- Beginning Sql Server R Services
- Behavior Analysis with Machine Learning Using R
- Behavioral Data Analysis with R and Python. Customer-Driven Data for Real Business Results
- Behavioral Research Data Analysis With R
- Benchmarking with DEA, SFA, and R
- Beyond Multiple Linear Regression. Applied Generalized Linear Models And Multilevel Models in R
- Beyond Spreadsheets With R
- Big Data Analytics in Oncology with R
- Big Data Analytics with R
- Big Data Analytics with R and Hadoop
- Bioinformatics with R Cookbook
- Biostatistical Design And Analysis Using R. A Practical Guide
- Biostatistics and Computer-based Analysis of Health Data using R
- Biostatistics for Epidemiology and Public Health Using R
- Biostatistics With R. An Introduction To Statistics Through Biological Data
- Blogdown. Creating Sites With R Markdown
- bookdown. Authoring Books and Technical Documents with RMarkdown
- Bootstrap Methods. With Applications in R
- Building a Recommendation System with R
- Business Analytics Using R. a Practical Approach
- Business Case Analysis With R
- Business Intelligence With R
- Business Statistics with Solutions in R
- Chemometrics With R
- Chemotaxis Modeling of Autoimmune Inflammation PDE Computer Analysis in R
- Circular Statistics in R
- Clinical Trial Data Analysis Using R
- Clinical Trial Data Analysis With R and Sas (2nd)
- Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R
- Community Ecology Analytical Methods Using R and Excel
- Comparative Approaches to Using R and Python for Statistical Data Analysis
- Comparing Groups Randomization and Bootstrap Methods Using R
- Competing Risks And Multistate Models With R
- Complex Surveys. A Guide To Analysis Using R
- Computational Actuarial Science with R
- Computational Aspects of Psychometric Methods With R
- Computational Biology. A Practical IntroductionTo BioData Processing And Analysis With Linux, MySQL And R
- Computational Finance With R
- Computational Finance. an Introductory Course With R
- Computational Genomics with R
- Computational methods for numerical analysis with R
- Computational Statistics. An Introduction to R
- Computerized Adaptive and Multistage Testing with R. Using Packages catR and mstR
- Contingency Table Analysis. Methods and Implementation Using R
- Corpus Linguistics and Statistics with R
- Correspondence Analysis And Data Coding With Java And R
- CRAN Recipes. DPLYR, Stringr, Lubridate, and RegEx in R
- Creating Blogs with Jekyll
- Crime by the Numbers. A Criminologist’s Guide to R
- Customer and Business Analytics. Applied Data Mining for Business Decision Making Using R
- Data Analysis and Graphics Using R. an Example-Based Approach
- Data Analysis for the Life Sciences With R
- Data Analysis in Medicine and Health Using R
- Data Analysis Using Hierarchical Generalized Linear Models With R
- Data Analysis Using Regression And Multilevel Hierarchical Models
- Data Analysis With R
- Data Analytics for the Social Sciences. Applications in R
- Data Analytics Using R
- Data Envelopment Analysis with R
- Data Manipulation With R
- Data Manipulation with R [Packt]
- Data Manipulation With R [Springer]
- Data Mashups In R
- Data Mining Algorithms, Explained Using R
- Data Mining and Business Analytics With R
- Data Mining Application With R
- Data Mining for Business Analytics. Concepts, Techniques, and Applications in R
- Data Mining With R. Learning With Case Studies
- Data Mining with Rattle and R
- Data Science And Big Data Analytics
- Data Science and Predictive Analytics. Biomedical and Health Applications using R (2nd)
- Data Science for Infectious Disease Data Analytics. An Introduction with R
- Data Science Foundations Tools and Techniques. Core Skills for Quantitative Analysis with R and Git
- Data Science in Education Using R
- Data Science in R. A Case Studies Approach to Computational Reasoning and Problem Solving
- Data Science in the Cloud with Microsoft Azure Machine Learning and R
- Data science using Python and R
- Data Science with R for Psychologists and Healthcare Professionals
- Data Science, Analytics and Machine Learning with R
- Data Visualisation with R
- Data Visualization and Exploration With R
- Data Visualization for Social and Policy Research A Step-by-Step Approach Using R and Python
- Data Wrangling with R [Packt]
- Data Wrangling with R [Springer]
- Deep Learning and Scientific Computing with R torch
- Deep Learning Made Easy With R. A Gentle Introduction For Data Science
- Deep Learning with R (2nd)
- Deep Learning With R [Manning]
- Deep Learning With R [Springer]
- Deep Learning With R Cookbook. Over 45 Unique Recipes to Delve Into Neural Network Techniques Using R 3.5.X
- Deep Learning. From Big Data to Artificial Intelligence with R
- Design and Analysis of Experiments and Observational Studies Using R
- Design and Analysis of Experiments with R
- Developing Data Products In R
- Discovering Statistics Using R
- Discrete Choice Analysis With R
- Displaying time series, spatial, and space-time data with R (2nd)
- Distributions for Modeling 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
- Domain-Specific Languages in R
- Dose-Response Analysis Using R
- Dynamic Documents with R and knitr
- Dynamic Linear Models With R
- Dynamic Time Series Models using R-INLA. An Applied Perspective
- Easy Statistics for Food Science With R
- Ecological Models and Data in R
- Efficient R Programming
- Elements of Copula Modeling with R
- Engineering Production-Grade Shiny Apps
- Ensemble Classification Methods With Applications in R
- Environmental and Ecological Statistics With R (2nd)
- Environmental Data Analysis. An Introduction with Examples in R
- EnvStats. An R Package for Environmental Statistics
- Epidemics. Models and Data Using R
- Event History Analysis with R (2nd)
- Exploratory Data Analysis Using R
- Exploratory Multivariate Analysis by Example Using R (2nd)
- Exploring Data Science with R and the Tidyverse, A Concise Introduction
- Exploring Everyday Things With R And Ruby
- Extending R
- Extending the Linear Model With R (2nd)
- Factor Analysis and Dimension Reduction in R
- Financial Analytics with R
- Financial Risk Modelling and Portfolio Optimization With R (2nd)
- Financial, Macro and Micro Econometrics Using R
- Flexible Regression and Smoothing Using GAMLSS in R
- Forecasting. Principles And Practice (3rd)
- Forest Analytics with R. An Introduction
- Foundational and Applied Statistics for Biologists using R
- Foundations and Applications of Statistics. An Introduction Using R (2nd)
- Foundations of Statistical Algorithms. With References to R Packages
- Foundations of Statistics for Data Scientists With R and Python
- Functional And Phylogenetic Ecology In R
- Functional Data Analysis With R And Matlab
- Functional Data Structures in R. Advanced Statistical Programming in R
- Functional Programming in R 4 (2nd)
- Fundamentals of Causal Inference with R
- Fundamentals of High-Dimensional Statistics. With Exercises and R Labs
- 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
- Geocomputation With R (2nd)
- Geographic Data Science with R
- Geostatistics for Compositional Data with R
- Getting Started with R. An Introduction for Biologists
- Getting Started With Rstudio
- ggplot2 Essentials
- ggplot2. Elegant Graphics for Data Analysis (2nd)
- Graphical data analysis with R
- Graphical Models With R
- Graphics for Statistics and Data Analysis with R
- Graphing Data With R
- Growth Curve Analysis and Visualization Using R
- Guide to Create Beautiful Graphics in R
- Guide To Programming And Algorithms Using R
- Guidebook To R Graphics Using Microsoft Windows
- Handbook of educational measurement and psychometrics using R
- Handbook Of Fitting Statistical Distributions With R
- Hands-On Data Analysis in R for Finance
- Hands-On Deep Learning with R
- Hands-On Exploratory Data Analysis with R
- Hands-on Machine Learning With R
- Hands-on matrix algebra using R
- Hands-on Programming With R
- Hands-on Time Series Analysis With R. Perform Time Series Analysis and Forecasting Using R
- Heart rate variability analysis with the R package RHRV
- Hidden Markov Models for Time Series. An Introduction Using R (2nd)
- Humanities Data In R
- Identifiability and Regression Analysis of Biological Systems Models. Statistical and Mathematical Foundations and R Scripts
- Implementing Reproducible Research
- Individual-Based Models of Cultural Evolution. A Step-by-Step Guide Using R
- Instant R Starter
- Integrated Population Models. Theory and Ecological Applications with R and JAGS
- Interactive And Dynamic Graphics For Data Analysis. With R And Ggobi
- Interactive Web-Based Data Visualization With R, Plotly, and Shiny
- Introducing Monte Carlo Methods With R
- Introduction To Data Analysis And Graphical Presentation In Biostatistics With R
- Introduction to Data Analysis with R for Forensic Scientists
- Introduction to Data Science, With Introduction to R, Version 3
- Introduction to Data Science. Data Analysis and Prediction Algorithms With R
- Introduction to Deep Learning Using R
- Introduction to GIS. Manipulating and Mapping Geospatial data With R
- Introduction To Image Processing Using R
- Introduction to Mathematics for Economics with R
- Introduction To Modern Portfolio Optimization With Nuopt, S-Plus And S Bayes
- Introduction to Nonparametric Statistics for the Biological Sciences Using R
- Introduction to Probabilistic and Statistical Methods with Examples in R
- Introduction To Probability And Statistics Using R
- Introduction To Probability Simulation And Gibbs Sampling With R
- Introduction to Probability with R
- Introduction to R for Business Intelligence
- Introduction to R for Quantitative Finance
- Introduction to R for Social Scientists. A Tidy Programming Approach
- Introduction to R Programming Language
- Introduction to Scientific Programming and Simulation Using R (2nd)
- Introduction To Statistical Data Analysis With R
- Introduction to Statistics and Data Analysis. With Exercises, Solutions and Applications in R (2nd)
- Introduction to Statistics through Resampling Methods and R
- Introduction to Stochastic Processes With R
- Introduction to Stochastic Processes with R (Solution Manual)
- Introduction to Time Series Modeling with Applications in R (2nd)
- Introduction to Unconstrained Optimization with R
- Introductory Applied Statistics. With Resampling Methods & R
- Introductory Fisheries Analyses With R
- Introductory R
- Introductory Statistics with R
- Introductory Time Series with R
- Investing in Mortgage-Backed and Asset-Backed Securities: Financial Modeling with R and Open Source Analytics
- Javascript for R
- Kernel Methods for Machine Learning with Math and R
- Latent Variable Modeling Using R
- Latent Variable Modeling With R
- Lattice. Multivariate Data Visualization With R
- Learn Business Analytics in Six Steps Using Sas and R
- Learn Ggplot2 Using Shiny App
- Learn R As a Language
- Learn R for Applied Statistics
- Learn RStudio IDE
- Learning Analytics in R with SNA, LSA, and MPIA
- Learning Bayesian Models with R
- Learning Data Mining With R
- Learning Microeconometrics with R
- Learning Predictive Analytics With R
- Learning Probabilistic Graphical Models in R
- Learning Quantitative Finance With R
- Learning R
- Learning R and Python for Business School Students
- Learning R for Geospatial Analysis
- Learning R Programming
- Learning Rstudio For R Statistical Computing
- Learning Shiny
- Learning Social Media Analytics with R
- Linear Algebra and Its Applications with R
- Linear Mixed-Effects Models Using R. A Step-by-Step Approach
- Linear Models With R
- Linear Regression Models. Applications in R
- Linear Regression Using R. An Introduction to Data Modeling (2nd)
- Luminescence Data Analysis and Modeling Using R
- Machine Learning Analysis of qPCR data using R
- Machine Learning Essentials. Practical Guide in R
- Machine Learning for Factor Investing. R Version
- Machine Learning Mastery With R
- Machine Learning Using R (2nd)
- Machine Learning with R [Packt] (4th)
- Machine Learning With R [Springer]
- Machine Learning With R Cookbook
- Machine Learning With R, Tidyverse, and Mlr
- Making Your Case. Using R for Program Evaluation
- Marketing Analytics. Optimize Your Business With Data Science in R, Python, and Sql
- Mastering Data Analysis With R
- Mastering Machine Learning With R
- Mastering Parallel Programming with R
- Mastering Predictive Analytics With R (2nd)
- Mastering R for Quantitative Finance
- Mastering RStudio. Develop, Communicate, and Collaborate with R
- Mastering Scientific Computing With R
- Mastering Social Media Mining with R
- Mastering Text Mining with R
- Mathematical Foundations of Data Science Using R (2nd)
- Mathematical Statistics With Applications in R (3rd)
- Mathematical Statistics with Resampling and R (2nd)
- Maximum Likelihood Estimation and Inference. With Examples in R, SAS and ADMB
- Measuring Productivity in Education and Not-for-Profits With Tools and Examples in R
- Meta-Analysis with R
- Metaprogramming in R
- Missing and Modified Data in Nonparametric Estimation. With R Examples
- Mixed Effect Models And Extensions In Ecology With R
- Mixed Models. Theory and Applications With R
- Model-Based Clustering and Classification for Data Science. With Applications in R
- Model-Based Clustering, Classification, and Density Estimation Using mclust in R
- Modeling Binary Correlated Responses using SAS, SPSS and R
- Modeling Psychophysical Data In R
- Modern Actuarial Risk Theory Using R
- Modern Analysis of Biological Data Generalized Linear Models in R
- Modern Analysis Of Customer Surveys
- Modern Applied Statistics With S
- Modern Data Science with R (2nd)
- Modern Industrial Statistics. With Applicationsin R, MINITAB, and JMP (3rd)
- Modern Optimization with R (2nd)
- Modern Psychometrics with R
- Modern R Programming Cookbook
- Modern Regression Techniques Using R
- Modern Statistical Methods For Astronomy. With R Applications
- Morphometrics With R
- Multilayer Networks Analysis and Visualization. Introduction to muxViz with R
- Multilevel Modeling Using R (2nd)
- Multiple Comparisons Using R
- Multiple Factor Analysis By Examples Using R
- Multistate Analysis of Life Histories with R
- Multivariate Data Integration. Using R Methods and Applications with the mixOmics Package
- Multivariate Generalized Linear Mixed Models Using R
- Multivariate Methods of Representing Relations in R for Prioritization Purposes
- Multivariate Nonparametric Regression And Visualization With R And Applications To Finance
- Multivariate Statistical Quality Control Using R
- Multivariate Time Series Analysis. With R And Financial Applications
- Nature in Silico Population Genetic Simulation and its Evolutionary Interpretation Using C++ and R
- Network Psychometrics with R
- Neural Networks for Time Series Forecasting With R
- New Statistics for Design Researchers. A Bayesian Workflow in Tidy R
- Nonlinear Parameter Optimization Using R Tools
- Nonlinear Regression With R
- Nonlinear Time Series Analysis With R
- Nonlinear Time Series Theory, Methods and Applications with R Examples
- Nonparametric Hypothesis Testing. Rank and Permutation Methods With Applications In R
- Nonparametric Statistical Methods Using R
- Nonparametric Statistics with Applications to Science and Engineering with R
- Numerical Analysis Using R. Solutions to ODEs and PDEs
- Numerical Ecology with R
- Openintro Statistics
- Optimal Experimental Design With R
- Optimization Modeling Using R
- Option Pricing and Estimation of Financial Models with R
- Outstanding User Interfaces with Shiny
- Panel Data Econometrics With R
- Parallel Computing for Data Science. With Examples in R, C++ and CUDA
- Parallel R
- Partial Least Squares Structural Equation Modeling Using R
- Permutation Statistical Methods with R
- Phylogenetic Comparative Methods in R
- Political Analysis Using R
- Population Genomics with R
- Practical Business Analytics Using R and Python
- Practical Data Science With R
- Practical Graph Mining With R
- Practical Guide to Cluster Analysis in R
- Practical Guide To Ecological Modelling. Using R As A Simulation Platform
- Practical Guide to Principal Component Methods in R
- Practical Machine Learning Cookbook
- Practical Machine Learning in R
- Practical R 4. Apply R to Data Manipulation, Processing, and Integration
- Practical R for Biologists. An Introduction
- Practical R for Mass Communication and Journalism
- Practical Regression and Anova using R
- Practical Statistics for Data Scientists. 50+ Essential Concepts Using R and Python (2nd)
- Practical Time Series Forecasting with R. A Hands-On Guide
- Practicing R for Statistical Computing
- Predictive Analytics Using R
- Predictive Modeling And Analytics
- Primer Of Ecology With R
- Primer to Analysis of Genomic Data Using R
- Pro Data Visualization Using R and JavaScript (2nd)
- Probability and Mathematical Statistics. Theory, Applications, and Practice in R
- Probability and Statistics for Data Science. Math + R + Data
- Probability and Statistics for Engineering and the Sciences with Modeling using R
- Probability and Statistics for Science and Engineering With Examples in R (2nd)
- Probability and Statistics With R (2nd)
- Probability and Statistics With R for Engineers and Scientists
- Probability With Applications and R (2nd)
- Probability, Decisions, and Games. a Gentle Introduction Using R
- Probability, Statistics and Simulation With Application Programs Written in R (Alberto Rotondi, Paolo Pedroni etc.) (z-lib.org)
- Probability, Statistics, and Data A Fresh Approach Using R
- Probability. With Applications And R (2nd)
- Production And Efficiency Analysis With R
- Productivity and Efficiency Measurement of Airlines. Data Envelopment Analysis using R
- Programming Graphical User Interfaces In R
- Programming With Data. A Guide To The S Language
- Python and R for the Modern Data Scientist. The Best of Both Worlds
- Python for R Users. a Data Science Approach
- QCA With R
- Qualitative Comparative Analysis Using R. A Beginners Guide
- Qualitative Comparative Analysis with R. A User’s Guide
- Qualitative Research Using R. A Systematic Approach
- Quality Control with R
- Quantile Regression for Cross-Sectional and Time Series Data Applications in Energy Markets Using R
- Quantile Regression Using R
- Quantitative Methods in Archaeology using R
- Quantitative Trading With R
- R 4 Data Science Quick Reference (2nd)
- R and Data Mining
- R And Matlab
- R By Example
- R Data Analysis Cookbook
- R Data Mining
- R Data Mining Blueprints
- R Data Science Essentials
- R Data Science Quick Reference
- R Data Structures And Algorithms
- R Data Visualization Cookbook
- R Deep Learning Essentials
- R For Business Analytics
- R For Cloud Computing
- R For Data Science
- R for Data Science Cookbook
- R for Everyone. Advanced Analytics and Graphics
- R for Marketing Research and Analytics
- R for Programmers
- R for Programmers. Advanced Techniques
- R for Programmers. Mastering the Tools
- R for Quantitative Chemistry
- R For Sas And Spss Users
- R For Stata Users
- R for Statistics
- R Graph Essentials
- R Graphics
- R Graphs Cookbook (2nd)
- R High Performance Programming
- R In A Nutshell
- R in Action. Data Analysis and Graphics with R and Tidyverse (3rd)
- R Machine Learning By Examples
- R Machine Learning Essentials
- R Mining Spatial, Text, Web, and Social Media Data
- R Object Oriented Programming
- R Packages
- R Programming By Example. Practical, hands-on projects to help you get started with R
- R Programming For Bioinformatics
- R Programming For Data Science
- R Quick Syntax Reference
- R Recipes. A Problem-Solution Approach
- R Statistical Application Development by Example. Beginner's Guide
- R Through Excel
- R. Data Analysis and Visualization
- R. Unleash Machine Learning Techniques
- Random Forests with R
- Random Process Analysis With R
- Rasch Measurement Theory Analysis in R
- Reasoning with data. An introduction to traditional and Bayesian statistics using R
- Regression Analysis With R
- Regression Models for Data Science in R
- Report Writing For Data Science In R
- Reproducible Econometrics Using R
- Reproducible Finance with R. Code Flows and Shiny Apps for Portfolio Analysis
- Reproducible Research With R and Rstudio (3rd)
- Robust Nonlinear Regression With Applications Using R
- Robust statistical methods with R
- Rstudio for R Statistical Computing Cookbook
- S Programming
- SAR Image Analysis. A Computational Statistics Approach With R
- Sas And R. Data Management, Statistical Analysis And Graphics
- SAS for R Users. A Book for Data Scientists
- Seamless R and C++ Integration with Rcpp
- Semiparametric Regression With R
- Simulation and inference for stochastic differential equations. With R examples
- Simulation for Data Science with R
- Six Sigma With R. Statistical Engineering For Process Improvement
- Social Media Mining with R
- Software For Data Analysis. Programming With R
- Solving Differential Equations In R
- Sound Analysis and Synthesis with R
- Spatial And Spatio-temporal Bayesian Models With R-INLA
- Spatial Data Analysis in Ecology and Agriculture Using R (2nd)
- Spatial Data Science With Applications in R
- Spatial Database For Gps Wildlife Tracking Data
- Spatial Ecology and Conservation Modeling. Applications With R
- Spatial Econometric Methods in Agricultural Economics Using R
- Spatial Microsimulation With R
- Spatial Modeling in Gis and R for Earth and Environmental Sciences
- Spatial Point Patterns. Methodology and Applications With R
- Spatial Predictive Modelling with R
- Spatial Relationships Between Two Georeferenced Variables. With Applications in R
- Spatial Sampling with R
- Spatio-Temporal Statistics With R
- Sports Analytics in Practice with R
- Stated Preference Methods Using R
- Statistical Analysis and Data Display. An Intermediate Course with Examples in R (2nd)
- Statistical Analysis of Climate Series Analyzing, Plotting, Modeling, and Predicting with R
- Statistical Analysis of Financial Data in R (2nd)
- Statistical Analysis Of Financial Data In S-Plus
- Statistical Analysis of Financial Data With Examples in R
- Statistical Analysis of Microbiome Data with R
- Statistical Analysis of Network Data With R (2nd)
- Statistical Analysis Of Questionnaires. A Unified Approach Based On R And Stata
- Statistical Analysis With R
- Statistical Analysis With R for Dummies
- Statistical Analytics for Health Data Science with SAS and R
- Statistical Bioinformatics With R
- Statistical Computing in C++ and R
- Statistical Computing with R (2nd)
- Statistical Data Analysis Explained. Applied Environmental Statistics With R
- Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery
- Statistical Data Cleaning With Applications in R
- Statistical Inference For Data Science
- Statistical Inference in Financial and Insurance Mathematics with R
- Statistical Inference via Data Science. A ModernDive into R and the Tidyverse
- Statistical Methods for Environmental Epidemiology With R
- Statistical Methods For Hospital Monitoring With R
- Statistical Methods for Mediation, Confounding and Moderation Analysis. Using R and SAS
- Statistical Methods for Survival Trial Design With Applications to Cancer Clinical Trials Using R
- Statistical Quality Control Using MINITAB, R, JMP and Python
- Statistical Rethinking. a Bayesian Course With Examples in R and Stan (2nd)
- Statistical Studies of Income Poverty and Inequality in Europe
- Statistical Tools For Nonlinear Regression. A Practical Guide With S-Plus And R Examples
- Statistics and Data Analysis for Financial Engineering. With R Examples (2nd)
- Statistics and Data Analysis for Microarrays Using R and Bioconductor
- Statistics And Data With R
- Statistics and Probability. With Applications for Engineers and Scientists Using Minitab, R and Jmp (2nd)
- Statistics For Censored Environmental Data Using Minitab And R
- Statistics for Linguistics with R
- Statistics for Linguists. An Introduction Using R
- Statistics for People Who (Think They) Hate Statistics Using R
- Statistics in Engineering With Examples in MATLAB and R (2nd)
- Statistics In Psychology Using R And SPSS
- Statistics in Toxicology Using R
- Statistics Playbook. Real NBA data using R
- Statistics Using R. An Integrative Approach
- Statistics. an Introduction Using R
- Structural Equation Modeling Using R-SAS. A Step-by-Step Approach with Real Data Analysis
- Structural Equation Modelling with Partial Least Squares Using Stata and R
- Sufficient Dimension Reduction. Methods and Applications With R
- Supervised Machine Learning for Text Analysis in R
- Supervised machine learning optimization framework and applications with SAS and R
- Surveying with Geomatics and R
- Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R, SAS, and BUGS
- System Dynamics Modeling with R
- Teach Yourself R in 24 Hours
- Telling Stories with Data. With Applications in R
- Testing R code
- Text Analysis With R for Students of Literature
- Text Mining in Practice With R
- Text Mining with R. A Tidy Approach
- Textual data science using R
- The Analysis of Time Series. An Introduction with R (7th)
- The Art Of Data Science
- The Art Of R Programming. A Tour Of Statistical Software Design
- The Basics of Item Response Theory Using R
- The Basics Of S And S-Plus
- The Book of R
- The Elements Of Data Analytics Style
- The Essential R Reference
- The Essentials of Data Science. Knowledge Discovery Using R
- The Fundamentals of People Analytics With Applications in R
- The Hitchhiker’s Guide to Ggplot2
- The New Statistics with R. An Introduction for Biologists
- The R Book (3rd)
- The R Primer (2nd)
- The R Software
- The R Student Companion
- The Statistical Analysis of Doubly Truncated Data. With Applications in R
- Tidy Finance with R
- Time Series Analysis and Its Applications. With R Examples (4th)
- Time Series Analysis. With Applications In R
- Time Series Forecasting with R. A Beginner’s Guide
- Time Series. A Data Analysis Approach Using R
- Time Series. Applications To Finance With R And S-Plus
- Tree-Based Methods for Statistical Learning in R
- Two-Way Analysis of Variance. Statistical Tests and Graphics Using R
- Uncertain Quantification using R
- Uncertainty Analysis of Experimental Data With R
- Understanding and applying basic statistical methods using R
- Understanding Statistics Using R
- Univariate, Bivariate, and Multivariate Statistics Using R. Quantitative Tools for Data Analysis and Data Science
- Unsupervised Learning With R
- Using R and Rstudio for Data Management, Statistical Analysis, and Graphics
- Using R at the Bench. Step-by-Step Data Analytics for Biologists
- Using R for Bayesian Spatial and Spatio-Temporal Health Modeling
- Using R for Biostatistics
- Using R for Data Analysis in Social Sciences. a Research Project-Oriented Approach
- Using R For Data Management, Statistical Analysis, And Graphics
- Using R for Introductory Econometrics (2nd)
- Using R for Introductory Statistics (2nd)
- Using R for Item Response Theory. Model Applications
- Using R for Modelling and Quantitative Methods in Fisheries
- Using R for Numerical Analysis in Science and Engineering
- Using R for Statistics
- Using R for Trade Policy Analysis. R Codes for the UNCTAD and WTO Practical Guide
- Using the R Commander. A Point-and-Click Interface for R
- Using the R package Shiny to create web applications that facilitate quantitative gene expression analysis
- Vector Generalized Linear And Additive Models With An Implementation In R
- Visualizing Complex Data Using R
- Visualizing Data in R 4 Graphics. Using the base, graphics, stats, and ggplot2 Packages
- Wavelet Methods In Statistics With R
- Web Application Development with R Using Shiny
- What You Need To Know about R
- Working with Data in Public Health. A Practical Pathway with R
- Working with the American Community Survey in R
- XML and Web Technologies for Data Sciences with R
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