R (Computer program language)
Overview
Works: | 415 works in 231 publications in 231 languages |
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Titles
An introduction to R :notes on R: a programming environment for data analysis and graphics, version 1.4.1 /
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Statistical analysis and data display :an intermediate course with examples in S-plus, R, and SAS /
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Interactive and Dynamic Graphics for Data Analysis :with R and GGobi /
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Statistical methods for environmental epidemiology with Ra case study in air pollution and health /
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A practical guide to ecological modellingusing R as a simulation platform /
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Applied statistical genetics with Rfor population-based association studies /
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Permutation tests for stochastic ordering and ANOVAtheory and applications with R /
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Statistics and data with R :an applied approach through examples /
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Hands-on intermediate econometrics using R :templates for extending dozens of practical examples /
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R through Excela spreadsheet interface for statistics, data analysis, and graphics /
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Multivariate nonparametric methods with Ran approach based on spatial signs and ranks /
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An introduction to statistical inference and its applications with R /
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Data mining with Rattle and Rthe art of excavating data for knowledge discovery /
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Biostatistics with Ran introduction to statistics through biological data /
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Multivariate methods of representing relations in R for prioritization purposesselective scaling, comparative clustering, collective criteria and sequenced sets /
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Combinatorial pattern matching algorithms in computational biology using Perl and R
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Statistical data analysis explainedapplied environmental statistics with R /
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Social media mining with R :deploy cutting-edge sentiment analysis techniques to real-world social media data using R /
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Introduction to data analysis and graphical presentation in biostatistics with Rstatistics in the large /
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Data science in R :a case studies approach to computational reasoning and problem solving /
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Parallel computing for data science :with examples in R, C++ and CUDA /
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An introduction to R for quantitative economicsgraphing, simulating and computing /
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Adaptive tests of significance using permutations of residuals with R and SAS
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Nonparametric hypothesis testingrank and permutation methods with applications in R /
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Mastering R for quantitative finance :use R to optimize your trading strategy and build up your own risk management system /
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Humanities data in Rexploring networks, geospatial data, images, and text /
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Statistical analysis and data displayan intermediate course with examples in R /
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Mastering predictive analytics with R :master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts /
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Marketing data science :modeling techniques in predictive analytics with R and Python /
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Adaptive tests of significance using permutations of residuals with R and SAS /
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Geochemical modelling of igneous processes - principles and recipes in R languagebringing the power of R to a geochemical community /
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Statistical analysis and data display :an intermediate course with examples in R /
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Bayesian data analysis in ecology using linear models withR, Bugs, and Stan
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Introduction to nonparametric statistics for the biological sciences using R
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Working with the American community survey in Ra guide to using the acs package /
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Learn business analytics in six steps using SAS and Ra practical, step-by-step guide to learning business analytics /
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Introduction to statistics and data analysiswith exercises, solutions and applications in R /
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Functional programming in Radvanced statistical programming for data science, analysis and finance /
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Joint models for longitudinal and time-to-event datawith applications in R /
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Using R and RStudio for data management, statistical analysis, and graphics
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Comparative approaches to using R and Python for statistical data analysis
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Statistical disclosure control for microdatamethods and applications in R /
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Metaprogramming in Radvanced statistical programming for data science, analysis and finance /
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Advanced object-oriented programming in Rstatistical programming for data science, analysis and finance /
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Introduction to deep learning using Ra step-by-step guide to learning and implementing deep learning models using R /
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Corpus linguistics and statistics with Rintroduction to quantitative methods in linguistics /
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Computerized adaptive and multistage testing with Rusing packages catR and mstR /
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Business case analysis with Rsimulation tutorials to support complex business decisions /
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Applied probabilistic calculus for financial engineeringan introduction using R /
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Simulation and inference for stochastic processes with YUIMAa comprehensive R framework for SDEs and other stochastic processes /
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Using R for data analysis in social sciences :a research project-oriented approach /
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Pro machine learning algorithmsa hands-on approach to implementing algorithms in Python and R /
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Applied analytics through case studies using SAS and Rimplementing predictive models and machine learning techniques /
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Data science and predictive analyticsbiomedical and health applications using R /
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Advanced statistics for the behavioral sciencesa computational approach wiith R /
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Learn R for applied statisticswith data visualizations, regressions, and statistics /
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Advanced R statistical programming and data modelsanalysis, machine learning, and visualization /
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Machine learning using Rwith time series and industry-based use cases in R /
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R quick syntax referencea pocket guide to the language, APIs and library /
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From experimental network to meta-analysismethods and applications with R for agronomic and environmental sciences /
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R data science quick referencea pocket guide to APIs, libraries, and packages /
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Discrete data analysis with R :visualization and modeling techniques for categorical and count data /
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Introduction to data science :data analysis and prediction algorithms with R /
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Model-based clustering and classification for data science :with applications in R /
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Practical time series analysis :prediction with statistics and machine learning /
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A data scientist's guide to acquiring, cleaning, and managing data in R /
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Statistical rethinking :a Bayesian course with examples in R and Stan /
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Statistics for machine learning :techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R /
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Multivariate time series analysis :with R and financial applications /
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Quantile regression for cross-sectional and time series dataapplications in energy markets using R /
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Introduction to R for terrestrial ecologybasics of numerical analysis, mapping, statistical tests and advanced application of R /
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Using R for trade policy analysisR codes for the UNCTAD and WTO practical guide /
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Applied multiple imputationadvantages, pitfalls, new developments and applications in R /
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Financial analytics with Rbuilding a laptop laboratory for data science /
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Applied hierarchical modeling in ecology.analysis of distribution, abundance and species richness in R and BUGS /Volume 1,Prelude and static models
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Statistical analysis of questionnairesa unified approach based on R and Stata /
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Advanced R 4 data programming and the cloudusing PostgreSQL, AWS, and Shiny /
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Chemometrics with Rmultivariate data analysis in the natural and life sciences /
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Spatial relationships between two georeferenced variableswith applications in R /
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Retirement income recipes in Rfrom ruin probabilities to intelligent drawdowns /
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Advanced analytics in power BI with R and Pythoningesting, transforming, visualizing /
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Practical R 4applying R to data manipulation, processing and integration /
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Insights from data with R :an introduction for the life and environmental sciences /
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Algebraic analysis of social networks :models, methods & applications using R /
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Using R for Bayesian spatial and spatio-temporal health modeling /
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Modern industrial statistics :with applications in R, MINITAB and JMP /
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A step-by-step guide to exploratory factor analysis with R and Rstudio /
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Statistical regression modeling with Rlongitudinal and multi-level modeling /
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Pricing export credita concise framework with examples and implementation code in R /
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A course on small area estimation and mixed modelsmethods, theory and applications in R /
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Measuring productivity in education and not-for-profitswith tools and examples in R /
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Univariate, bivariate, and multivariate statistics using Rquantitative tools for data analysis and data science /
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Partial least squares structural equation modeling (PLS-SEM) using Ra workbook /
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Virus host cell genetic material transportcomputational ode/pde modeling with R /
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Pro data visualization using R and Javascriptanalyze and visualize key data on the web /
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Kernel methods for machine learning with Math and R100 exercises for building logic /
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Multilayer networksanalysis and visualization : introduction to muxViz with R /
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R 4 quick syntax referencea pocket guide to the language, API's and library /
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Applied social network analysis with Remerging research and opportunities /
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Applied hierarchical modeling in ecologyanalysis of distribution, abundance and species richness in R and BUGS.Volume 2,Dynamic and advanced models /
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Beginning data science in R 4data analysis, visualization, and modelling for the data scientist /
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Data visualization for social and policy researcha step-by-step approach using R and Python /
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Supervised machine learningoptimization framework and applications with SAS and R /
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R 4 data science quick referencea pocket guide to APIs, libraries, and packages /
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Probability, statistics and simulationwith application programs written in R /
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Probability, statistics and simulation :with application programs written in R /
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Practical statistics for data scientists :50+ essential concepts using R and Python /
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Essentials of Excel VBA, Python, and R.Volume II,Financial derivatives, risk management and machine learning
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Practical business analytics using R and Pythonsolve business problems using a data-driven approach /
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Hyperparameter tuning for machine and deep learning with Ra practical guide /
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Applied linear regression for business analytics with Ra practical guide to data science with case studies /
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Functional programming in R 4advanced statistical programming for data science, analysis, and finance /
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Spatial socio-econometric modeling (SSEM)a low-code toolkit for spatial data science and interactive visualizations using R /
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Elements of data science, machine learning, and artificial intelligence using R
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Spatio-temporal trend analysis of rainfall using R software and ArcGISa case study of an agro-climatic zone-1 of Gujarat, India /
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Bayesian multilevel models for repeated measures data :a conceptual and practical introduction in R /
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The statistical analysis of doubly truncated data :with applications in R /
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Handbook of graphs and networks in people analytics :with examples in R and python /
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Regression-based normative data for psychological assessmenta hands-on approach using R /
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