R (Computer program language)
Overview
Works: | 350 works in 181 publications in 181 languages |
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Titles
Permutation tests for stochastic ordering and ANOVA = theory and applications with R /
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Adaptive tests of significance using permutations of residuals with R and SAS /
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Biostatistics with R = an introduction to statistics through biological data /
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A Practical guide to ecological modelling : = using R as a simulation platform /
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The Art of R programming : = a tour of statistical software design /
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Combinatorial pattern matching algorithms in computational biology using Perl and R
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Introduction to data analysis and graphical presentation in biostatistics with R = statistics in the large /
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A Beginner's guide to GLM and GLMM with R : = a frequentist and Bayesian perspective for ecologists /
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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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Automated data collection with R : = a practical guide to Web scraping and text mining /
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An introduction to R for quantitative economics = graphing, simulating and computing /
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Statistical analysis of questionnaires : = a unified approach based on R and Stata /
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Modern industrial statistics : = with applications in R, MINITAB and JMP /
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Geochemical modelling of igneous processes - principles and recipes in R language = bringing the power of R to a geochemical community /
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Introduction to nonparametric statistics for the biological sciences using R
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Learn business analytics in six steps using SAS and R = a practical, step-by-step guide to learning business analytics /
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Introduction to statistics and data analysis = with exercises, solutions and applications in R /
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Functional programming in R = advanced statistical programming for data science, analysis and finance /
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Computerized adaptive and multistage testing with R = using packages catR and mstR /
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Business case analysis with R = simulation tutorials to support complex business decisions /
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Applied probabilistic calculus for financial engineering = an introduction using R /
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Machine learning using R = with time series and industry-based use cases in R /
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From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /
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R data science quick reference = a pocket guide to APIs, libraries, and packages /
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Financial analytics with R = building a laptop laboratory for data science /
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Introduction to R for terrestrial ecology = basics of numerical analysis, mapping, statistical tests and advanced application of R /
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Statistical analysis of questionnaires = a unified approach based on R and Stata /
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Advanced R 4 data programming and the cloud = using PostgreSQL, AWS, and Shiny /
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Statistical regression modeling with R = longitudinal and multi-level modeling /
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Statistical data analysis explained : = applied environmental statistics with R /
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Statistical methods for environmental epidemiology with R = a case study in air pollution and health /
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Hidden Markov models for time series : = an introduction using R /
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Analyzing linguistic data : = a practical introduction to statistics using R /
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A practical guide to ecological modelling = using R as a simulation platform /
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Applied statistical genetics with R = for population-based association studies /
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Multivariate nonparametric methods with R = an approach based on spatial signs and ranks /
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SAS and R : = data management, statistical analysis, and graphics /
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An introduction to statistical inference and its applications with R /
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Multivariate methods of representing relations in R for prioritization purposes = selective scaling, comparative clustering, collective criteria and sequenced sets /
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An Introduction to statistical learning : = with applications in R /
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Statistical data analysis explained = applied environmental statistics with R /
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Adaptive tests of significance using permutations of residuals with R and SAS
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Nonparametric hypothesis testing = rank and permutation methods with applications in R /
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Humanities data in R = exploring networks, geospatial data, images, and text /
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Statistical analysis and data display = an intermediate course with examples in R /
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Statistical rethinking : = a Bayesian course with examples in R and Stan /
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Introductory adaptive trial designs : = a practical guide with R /
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Bayesian data analysis in ecology using linear models withR, Bugs, and Stan
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Automated trading with R = quantitative research and platform development /
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Working with the American community survey in R = a guide to using the acs package /
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Comparative approaches to using R and Python for statistical data analysis
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Joint models for longitudinal and time-to-event data = with applications in R /
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Using R and RStudio for data management, statistical analysis, and graphics
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Statistical disclosure control for microdata = methods and applications in R /
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Introduction to deep learning using R = a step-by-step guide to learning and implementing deep learning models using R /
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Big data analytics with R : = utilize R to uncover hidden patterns in your big data /
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R for data science : = import, tidy, transform, visualize, and model data /
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Corpus linguistics and statistics with R = introduction to quantitative methods in linguistics /
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Functional data structures in R = advanced statistical programming in R /
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Simulation and inference for stochastic processes with YUIMA = a comprehensive R framework for SDEs and other stochastic processes /
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Applied analytics through case studies using SAS and R = implementing predictive models and machine learning techniques /
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Learn R for applied statistics = with data visualizations, regressions, and statistics /
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Advanced R statistical programming and data models = analysis, machine learning, and visualization /
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R quick syntax reference = a pocket guide to the language, APIs and library /
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Using R for trade policy analysis = R codes for the UNCTAD and WTO practical guide /
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Applied multiple imputation = advantages, pitfalls, new developments and applications in R /
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Quantile regression for cross-sectional and time series data = applications in energy markets using R /
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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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A course on small area estimation and mixed models = methods, theory and applications in R /
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Pricing export credit = a concise framework with examples and implementation code in R /
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Spatial relationships between two georeferenced variables = with applications in R /
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Retirement income recipes in R = from ruin probabilities to intelligent drawdowns /
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Measuring productivity in education and not-for-profits = with tools and examples in R /
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Chemometrics with R = multivariate data analysis in the natural and life sciences /
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Practical R 4 = applying R to data manipulation, processing and integration /
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Partial least squares structural equation modeling (PLS-SEM) using R = a workbook /
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The big R-book : = from data science to learning machines and big data /
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R crash course for biologists : = an introduction to R for bioinformatics and biostatistics /
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Virus host cell genetic material transport = computational ode/pde modeling with R /
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R 4 quick syntax reference = a pocket guide to the language, API's and library /
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Kernel methods for machine learning with Math and R = 100 exercises for building logic /
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Applied social network analysis with R = emerging research and opportunities /
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Hyperparameter tuning for machine and deep learning with R = a practical guide /
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Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning
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Functional programming in R 4 = advanced statistical programming for data science, analysis, and finance /
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Elements of data science, machine learning, and artificial intelligence using R
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Visualization and imputation of missing values = with applications in R /
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Advanced analytics in power BI with R and Python = ingesting, transforming, visualizing /
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Extending Power BI with Python and R : = ingest, transform, enrich and visualize data using the power of analytic languages /
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Pro data visualization using R and Javascript = analyze and visualize key data on the web /
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Multilayer networks = analysis and visualization : introduction to muxViz with R /
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Beginning data science in R 4 = data analysis, visualization, and modelling for the data scientist /
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R 4 data science quick reference = a pocket guide to APIs, libraries, and packages /
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Probability, statistics and simulation = with application programs written in R /
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Applied hierarchical modeling in ecology = analysis of distribution, abundance and species richness in R and BUGS.. Volume 2,. Dynamic and advanced models /
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Supervised machine learning = optimization framework and applications with SAS and R /
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Habitat suitability and distribution models : = with applications in R /
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Practical business analytics using R and Python = solve business problems using a data-driven approach /
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Data visualization for social and policy research = a step-by-step approach using R and Python /
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Applied linear regression for business analytics with R = a practical guide to data science with case studies /
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