R (Computer program language)

Morphometrics with R by: (Language materials, printed)

Statistical methods for environmental epidemiology with R = a case study in air pollution and health / by: (Language materials, printed)

Introductory statistics with R by: (Language materials, printed)

Introductory time series with R / by: (Language materials, printed)

A practical guide to ecological modelling = using R as a simulation platform / by: (Language materials, printed)

Mixed effects models and extensions in ecology with R by: (Language materials, printed)

R for SAS and SPSS users by: (Language materials, printed)

A modern approach to regression with R by: (Language materials, printed)

Applied statistical genetics with R = for population-based association studies / by: (Language materials, printed)

Permutation tests for stochastic ordering and ANOVA = theory and applications with R / by: (Language materials, printed)

Bayesian computation with R by: (Language materials, printed)

Introducing Monte Carlo methods with R by: (Language materials, printed)

Introductory statistics with R by: (Language materials, printed)

Ggplot2 = elegant graphics for data analysis / by: (Language materials, printed)

R for Stata users by: (Language materials, printed)

Ggplot2 : = elegant graphics for data analysis / by: (Language materials, printed)

Wavelet methods in statistics with R by: (Language materials, printed)

An introduction to applied multivariate analysis with R by: (Electronic resources)

Forest analytics with R = an introduction / by: (Electronic resources)

Numerical ecology with R by: (Electronic resources)

A tiny handbook of R by: (Electronic resources)

Handbook of fitting statistical distributions with R / by: (Language materials, printed)

Behavioral research data analysis with R by: (Electronic resources)

Competing risks and multistate models with R by: (Electronic resources)

Analysis of phylogenetics and evolution with R by: (Electronic resources)

Biostatistics with R = an introduction to statistics through biological data / by: (Electronic resources)

R by example = concepts to code / by: (Electronic resources)

Graphical models with R by: (Electronic resources)

Multivariate methods of representing relations in R for prioritization purposes = selective scaling, comparative clustering, collective criteria and sequenced sets / by: (Electronic resources)

The Art of R programming : = a tour of statistical software design / by: (Language materials, printed)

Hidden Markov models for time series = an introduction using R / by: (Electronic resources)

SAS and R = data management, statistical analysis, and graphics / by: (Electronic resources)

Multiple decrement models in Insurance = an introduction using R / by: (Electronic resources)

Modeling psychophysical data in R by: (Electronic resources)

Beginning R = an introduction to statistical programming / by: (Electronic resources)

An Introduction to statistical learning : = with applications in R / by: (Language materials, printed)

Statistical data analysis explained = applied environmental statistics with R / by: (Electronic resources)

A handbook of statistical analyses using R by: (Electronic resources)

Data mining with R = learning with case studies / by: (Electronic resources)

Exploratory multivariate analysis by example using R by: (Electronic resources)

Introduction to data analysis with R for forensic scientists by: (Electronic resources)

An Introduction to bootstrap methods with applications to R / by: (Language materials, printed)

XML and web technologies for data sciences with R by: (Electronic resources)

Introduction to data analysis and graphical presentation in biostatistics with R = statistics in the large / by: (Electronic resources)

R cookbook / by: (Language materials, printed)

R quick syntax reference by: (Electronic resources)

Using R for introductory statistics / by: (Language materials, printed)

Contingency table analysis = methods and implementation using R / by: (Electronic resources)

Statistical analysis of network data with R by: (Electronic resources)

Using R for statistics by: (Electronic resources)

Text analysis with R for students of literature by: (Electronic resources)

Statistics for linguistics with R = a practical introduction / by: (Electronic resources)

Data mining applications with R by: (Electronic resources)

Getting Started with R = An introduction for biologists / by: (Electronic resources)

Optimal experimental design with R by: (Electronic resources)

Modern optimization with R by: (Electronic resources)

Multistate analysis of life histories with R by: (Electronic resources)

Data science in R : = a case studies approach to computational reasoning and problem solving / by: (Language materials, printed)

A Handbook of statistical analyses using R / by: (Language materials, printed)

Parallel computing for data science : = with examples in R, C++ and CUDA / by: (Language materials, printed)

R Recipes = A Problem-Solution Approach / by: (Electronic resources)

Applied spatial data analysis with R / by: (Language materials, printed)

Financial risk modelling and portfolio optimization with R / by: (Language materials, printed)

Mathematical statistics with applications in R / by: (Language materials, printed)

Empirical likelihood method in survival analysis / by: (Language materials, printed)

Spatial and spatio-temporal Bayesian models with R-INLA / by: (Language materials, printed)

Using R with multivariate statistics / by: (Language materials, printed)

Probability and statistics with R / by: (Language materials, printed)

Automated data collection with R : = a practical guide to Web scraping and text mining / by: (Language materials, printed)

Data mining algorithms : = explained using R / by: (Language materials, printed)

An introduction to R for quantitative economics = graphing, simulating and computing / by: (Electronic resources)

R for marketing research and analytics by: (Electronic resources)

Spatial data analysis in ecology and agriculture using R by: (Electronic resources)

Analyzing financial data and implementing financial models using R by: (Electronic resources)

Adaptive tests of significance using permutations of residuals with R and SAS by: (Electronic resources)

Nonparametric hypothesis testing = rank and permutation methods with applications in R / by: (Electronic resources)

Modern industrial statistics = with applications in R, MINITAB and JMP / by: (Electronic resources)

Doing Bayesian data analysis = a tutorial with R, JAGS, and stan / by: (Electronic resources)

Primer to analysis of genomic Data using R by: (Electronic resources)

Applied multivariate statistics with R by: (Electronic resources)

Basics of matrix algebra for statistics with R / by: (Language materials, printed)

R and MATLAB / by: (Language materials, printed)

Spatial point patterns : = methodology and applications with R / by: (Language materials, printed)

Dynamic documents with R and knitr / by: (Language materials, printed)

Graphical data analysis with R / by: (Language materials, printed)

Multiple factor analysis by example using R / by: (Language materials, printed)

Event history analysis with R / by: (Language materials, printed)

Nonparametric statistical methods using R / by: (Language materials, printed)

Humanities data in R = exploring networks, geospatial data, images, and text / by: (Electronic resources)

Meta-analysis with R by: (Electronic resources)

Quality control with R = an ISO standards approach / by: (Electronic resources)

Beginning R = an introduction to statistical programming / by: (Electronic resources)

Statistical analysis of questionnaires : = a unified approach based on R and Stata / by: (Language materials, printed)

Statistical rethinking : = a Bayesian course with examples in R and Stan / by: (Language materials, printed)

Statistics in toxicology using R / by: (Language materials, printed)

Reproducible research with R and RStudio / by: (Language materials, printed)

Introductory adaptive trial designs : = a practical guide with R / by: (Language materials, printed)

Modern industrial statistics : = with applications in R, MINITAB and JMP / by: (Language materials, printed)

Analyzing sensory data with R / by: (Language materials, printed)

Statistical hypothesis testing with SAS and R / by: (Language materials, printed)

Geochemical modelling of igneous processes - principles and recipes in R language = bringing the power of R to a geochemical community / by: (Electronic resources)

Learning analytics in R with SNA, LSA, and MPIA by: (Electronic resources)

R data analysis without programming by: (Electronic resources)

Bayesian data analysis in ecology using linear models withR, Bugs, and Stan by: (Electronic resources)

System dynamics modeling with R by: (Electronic resources)

Ggplot2 = elegrant graphics for data analysis / by: (Electronic resources)

Introduction to stochastic processes with R by: (Electronic resources)

A Course in statistics with R / by: (Language materials, printed)

The New statistics with R : = an introduction for biologists / by: (Language materials, printed)

Working with the American community survey in R = a guide to using the acs package / by: (Electronic resources)

An introduction to data analysis using aggregation functions in R by: (Electronic resources)

Advanced R = data programming and the cloud / by: (Electronic resources)

Introduction to statistics and data analysis = with exercises, solutions and applications in R / by: (Electronic resources)

Learn ggplot2 using shiny app by: (Electronic resources)

Exploratory multivariate analysis by example using R / by: (Language materials, printed)

Flexible regression and smoothing : = using GAMLSS in R / by: (Language materials, printed)

Modern data science with R / by: (Language materials, printed)

Applied time series analysis with R / by: (Language materials, printed)

Clinical trial data analysis using R and SAS / by: (Language materials, printed)

Statistical analysis with R / by: (Language materials, printed)

Machine learning using R by: (Electronic resources)

Business analytics using R - a practical approach by: (Electronic resources)

Data visualisation with R = 100 examples / by: (Electronic resources)

Applying test equating methods = using R / by: (Electronic resources)

Functional programming in R = advanced statistical programming for data science, analysis and finance / by: (Electronic resources)

Comparative approaches to using R and Python for statistical data analysis by: (Electronic resources)

Joint models for longitudinal and time-to-event data = with applications in R / by: (Electronic resources)

Using R and RStudio for data management, statistical analysis, and graphics by: (Electronic resources)

Biostatistics and computer-based analysis of health data using R by: (Electronic resources)

Statistical disclosure control for microdata = methods and applications in R / by: (Electronic resources)

Bayesian cost-effectiveness analysis with the R package BCEA by: (Electronic resources)

Introduction to deep learning using R = a step-by-step guide to learning and implementing deep learning models using R / by: (Electronic resources)

Basic elements of computational statistics by: (Electronic resources)

Machine learning with R by: (Electronic resources)

Doubly classified model with R by: (Electronic resources)

Numerical ecology with R by: (Electronic resources)

Business case analysis with R = simulation tutorials to support complex business decisions / by: (Electronic resources)

R for programmers = mastering the tools / by: (Electronic resources)

Python for R users = a data science approach / by: (Electronic resources)

Simulation and inference for stochastic processes with YUIMA = a comprehensive R framework for SDEs and other stochastic processes / by: (Electronic resources)

Applied analytics through case studies using SAS and R = implementing predictive models and machine learning techniques / by: (Electronic resources)

QCA with R = a comprehensive resource / by: (Electronic resources)

Learn R for applied statistics = with data visualizations, regressions, and statistics / by: (Electronic resources)

Machine learning using R = with time series and industry-based use cases in R / by: (Electronic resources)

Advanced R statistical programming and data models = analysis, machine learning, and visualization / by: (Electronic resources)

R for finite element analyses of size-dependent microscale structures by: (Electronic resources)

Numerical analysis using R = solutions to ODEs and PDEs / by: (Electronic resources)

Qualitative research using R = a systematic approach / by: (Electronic resources)

From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences / by: (Electronic resources)

Analyzing dependent data with vine copulas = a practical guide with R / by: (Electronic resources)

Financial analytics with R = building a laptop laboratory for data science / by: (Electronic resources)

Data envelopment analysis with R by: (Electronic resources)

Introduction to R for terrestrial ecology = basics of numerical analysis, mapping, statistical tests and advanced application of R / by: (Electronic resources)

Using R for trade policy analysis = R codes for the UNCTAD and WTO practical guide / by: (Electronic resources)

Quantitative economics with R = a data science approach / by: (Electronic resources)

Applied multiple imputation = advantages, pitfalls, new developments and applications in R / by: (Electronic resources)

Quantile regression for cross-sectional and time series data = applications in energy markets using R / by: (Electronic resources)

Text analysis with R = for students of literature / by: (Electronic resources)

Introduction to unconstrained optimization with R by: (Electronic resources)

The R primer by: (Electronic resources)

Statistical analysis of questionnaires = a unified approach based on R and Stata / by: (Electronic resources)

Advanced R 4 data programming and the cloud = using PostgreSQL, AWS, and Shiny / by: (Electronic resources)

Applied hierarchical modeling in ecology. = analysis of distribution, abundance and species richness in R and BUGS /. Volume 1,. Prelude and static models by: (Electronic resources)

Easy statistics for food science with R by: (Electronic resources)

Exploratory multivariate analysis by example using R by: (Electronic resources)

R and MATLAB by: (Electronic resources)

Applied meta-analysis with R and Stata / by: (Language materials, printed)

Using R for biostatistics by: (Electronic resources)

A course on small area estimation and mixed models = methods, theory and applications in R / by: (Electronic resources)

Statistical regression modeling with R = longitudinal and multi-level modeling / by: (Electronic resources)

CRAN recipes = DPLYR, Stringr, Lubridate, and RegEx in R / by: (Electronic resources)

Pricing export credit = a concise framework with examples and implementation code in R / by: (Electronic resources)

Luminescence = data analysis and modeling using R / by: (Electronic resources)

Sparse estimation with math and R = 100 exercises for building logic / by: (Electronic resources)

Random forests with R by: (Electronic resources)

Spatial relationships between two georeferenced variables = with applications in R / by: (Electronic resources)

Retirement income recipes in R = from ruin probabilities to intelligent drawdowns / by: (Electronic resources)

Measuring productivity in education and not-for-profits = with tools and examples in R / by: (Electronic resources)

Analyzing financial data and implementing financial models using R by: (Electronic resources)

Statistical methods for overdispersed count data by: (Electronic resources)

Business statistics with solutions in R by: (Electronic resources)

Biomarker analysis in clinical trials with R by: (Electronic resources)

Bootstrap methods = with applications in R / by: (Electronic resources)

Permutation statistical methods with R by: (Electronic resources)

Applied statistics = theory and problem solutions with R / by: (Electronic resources)

Chemometrics with R = multivariate data analysis in the natural and life sciences / by: (Electronic resources)

An introduction to clustering with R by: (Electronic resources)

Advanced analytics in power BI with R and Python = ingesting, transforming, visualizing / by: (Electronic resources)

Beginning R 4 = from beginner to pro / by: (Electronic resources)

Practical R 4 = applying R to data manipulation, processing and integration / by: (Electronic resources)

Partial least squares structural equation modeling (PLS-SEM) using R = a workbook / by: (Electronic resources)

Geostatistics for compositional data with R by: (Electronic resources)

The big R-book : = from data science to learning machines and big data / by: (Language materials, printed)

Deep learning with R / by: (Language materials, printed)

Extending Power BI with Python and R : = ingest, transform, enrich and visualize data using the power of analytic languages / by: (Language materials, printed)

Pro data visualization using R and Javascript = analyze and visualize key data on the web / by: (Electronic resources)

Multilayer networks = analysis and visualization : introduction to muxViz with R / by: (Electronic resources)

Kernel methods for machine learning with Math and R = 100 exercises for building logic / by: (Electronic resources)

Mixture and hidden markov models with R by: (Electronic resources)

Introduction to mathematics for economics with R by: (Electronic resources)

Bayes factors for forensic decision analyses with R by: (Electronic resources)

Applied multivariate statistics with R by: (Electronic resources)

Applied hierarchical modeling in ecology = analysis of distribution, abundance and species richness in R and BUGS.. Volume 2,. Dynamic and advanced models / by: (Electronic resources)

Applied social network analysis with R = emerging research and opportunities / by: (Electronic resources)

R for health data science by: (Electronic resources)

Epidemics = models and data using R / by: (Electronic resources)

Construction analytics = forecasting and investment valuation / by: (Electronic resources)

Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning by: (Electronic resources)

Computational finance with R by: (Electronic resources)

Doing meta-analysis with R : = a hands-on guide / by: (Language materials, printed)

R markdown cookbook / by: (Language materials, printed)

Bayesian modeling of spatio-temporal data with R / by: (Language materials, printed)

Fundamentals of causal inference : = with R / by: (Language materials, printed)

Data visualization for social and policy research = a step-by-step approach using R and Python / by: (Electronic resources)

Applied linear regression for business analytics with R = a practical guide to data science with case studies / by: (Electronic resources)

Applied calculus with R by: (Electronic resources)

Functional programming in R 4 = advanced statistical programming for data science, analysis, and finance / by: (Electronic resources)

Spatial socio-econometric modeling (SSEM) = a low-code toolkit for spatial data science and interactive visualizations using R / by: (Electronic resources)

Practicing R for statistical computing by: (Electronic resources)

Biostatistics with 'R' = a guide for medical doctors / by: (Electronic resources)

WAIC and WBIC with R Stan = 100 exercises for building logic / by: (Electronic resources)

Elements of data science, machine learning, and artificial intelligence using R by: (Electronic resources)

Spatio-temporal trend analysis of rainfall using R software and ArcGIS = a case study of an agro-climatic zone-1 of Gujarat, India / by: (Electronic resources)

Visualization and imputation of missing values = with applications in R / by: (Electronic resources)

Behavior analysis with machine learning using R by: (Electronic resources)

Regression-based normative data for psychological assessment = a hands-on approach using R / by: (Electronic resources)

Geographic data analysis using R by: (Electronic resources)

Complex-valued econometrics with examples in R = modelling, regression and applications / by: (Electronic resources)

Artificial intelligence and machine learning with R = applications in the field of business analytics / by: (Electronic resources)

Introduction to statistics in human performance = using SPSS and R / by: (Electronic resources)

Model-based clustering and classification for data science = with applications in R / by: (Electronic resources)

Statistical data analysis explained : = applied environmental statistics with R / by: (Language materials, printed)

Statistical computing with R / by: (Language materials, printed)

Using R for introductory statistics / by: (Language materials, printed)

A modern approach to regression with R / by: (Language materials, printed)

Applied econometrics with R / by: (Language materials, printed)

A beginner's guide to R / by: (Language materials, printed)

Bayesian computation with R / by: (Language materials, printed)

Hidden Markov models for time series : = an introduction using R / by: (Language materials, printed)

Statistical modelling in R / by: (Language materials, printed)

Analyzing linguistic data : = a practical introduction to statistics using R / by: (Language materials, printed)

Introductory time series with R by: (Language materials, printed)

Multivariate nonparametric methods with R = an approach based on spatial signs and ranks / by: (Language materials, printed)

Introduction to probability simulation and Gibbs sampling with R by: (Language materials, printed)

Introducing Monte Carlo methods with R / by: (Language materials, printed)

R graphics / by: (Language materials, printed)

Using R for data management, statistical analysis, and graphics by: (Language materials, printed)

SAS and R : = data management, statistical analysis, and graphics / by: (Language materials, printed)

Numerical ecology with R / by: (Language materials, printed)

Analysis of phylogenetics and evolution with R / by: (Language materials, printed)

Adaptive tests of significance using permutations of residuals with R and SAS / by: (Language materials, printed)

Guidebook to R graphics using Microsoft Windows / by: (Language materials, printed)

Multiple comparisons using R / by: (Language materials, printed)

Programming graphical user interfaces in R / by: (Language materials, printed)

R graphics / by: (Language materials, printed)

Environmental and ecological statistics with R / by: (Language materials, printed)

Data mining with R : = learning with case studies / by: (Language materials, printed)

Introduction to data analysis with R for forensic scientists / by: (Language materials, printed)

Introduction to scientific programming and simulation using R / by: (Language materials, printed)

An introduction to statistical inference and its applications with R / by: (Language materials, printed)

Computational statistics : = an introduction to R / by: (Language materials, printed)

Exploratory multivariate analysis by example using R / by: (Language materials, printed)

Graphics for statistics and data analysis with R / by: (Language materials, printed)

Two-way analysis of variance = statistical tests and graphics using R / by: (Electronic resources)

A Practical guide to ecological modelling : = using R as a simulation platform / by: (Language materials, printed)

A Primer of ecology with R / by: (Language materials, printed)

Introduction to scientific programming and simulation using R by: (Electronic resources)

R programming for bioinformatics by: (Electronic resources)

Combinatorial pattern matching algorithms in computational biology using Perl and R by: (Electronic resources)

Six sigma with R = statistical engineering for process improvement / by: (Electronic resources)

R and data mining = examples and case studies / by: (Electronic resources)

Handbook of fitting statistical distributions with R by: (Electronic resources)

Multiple comparisons using R by: (Electronic resources)

Clinical trial data analysis using R by: (Electronic resources)

Reproducible research with R and RStudio / by: (Language materials, printed)

Applied meta-analysis with R / by: (Language materials, printed)

Seamless R and C++ integration with Rcpp / by: (Language materials, printed)

Getting started with R : = an introduction for biologists / by: (Language materials, printed)

Bayesian essentials with R by: (Electronic resources)

Statistical analysis of financial data in R by: (Electronic resources)

Computational finance = an introductory course with R / by: (Electronic resources)

Business analytics and data mining with R by: (Electronic resources)

The R primer by: (Electronic resources)

An R companion to linear statistical models by: (Electronic resources)

Analysis of questionnaire data with R by: (Electronic resources)

R for cloud computing = an approach for data scientists / by: (Electronic resources)

A Beginner's guide to GLM and GLMM with R : = a frequentist and Bayesian perspective for ecologists / by: (Language materials, printed)

Beginning data science with R by: (Electronic resources)

Basic data analysis for time series with R by: (Electronic resources)

Nonlinear parameter optimization using R tools by: (Electronic resources)

Mathematical statistics with applications in R by: (Electronic resources)

Statistical analysis and data display = an intermediate course with examples in R / by: (Electronic resources)

Political analysis using R by: (Electronic resources)

A user's guide to network analysis in R by: (Electronic resources)

Production and efficiency analysis with R by: (Electronic resources)

Foundational and applied statistics for biologists using R / by: (Language materials, printed)

R graphics by: (Electronic resources)

Examples in parametric inference with R by: (Electronic resources)

Introduction to nonparametric statistics for the biological sciences using R by: (Electronic resources)

Data mining algorithms = explained using R / by: (Electronic resources)

Spatial and spatio-temporal Bayesian models with R-INLA by: (Electronic resources)

Data visualisation with R : = 100 examples / by: (Language materials, printed)

Automated trading with R = quantitative research and platform development / by: (Electronic resources)

Learn business analytics in six steps using SAS and R = a practical, step-by-step guide to learning business analytics / by: (Electronic resources)

The R primer / by: (Language materials, printed)

Testing R code / by: (Language materials, printed)

Using R for digital soil mapping by: (Electronic resources)

The basics of item response theory using R by: (Electronic resources)

Financial risk modelling and portfolio optimization with R / by: (Electronic resources)

Big data analytics with R : = utilize R to uncover hidden patterns in your big data / by: (Language materials, printed)

R for data science : = import, tidy, transform, visualize, and model data / by: (Language materials, printed)

Corpus linguistics and statistics with R = introduction to quantitative methods in linguistics / by: (Electronic resources)

Computerized adaptive and multistage testing with R = using packages catR and mstR / by: (Electronic resources)

Functional data structures in R = advanced statistical programming in R / by: (Electronic resources)

Applied probabilistic calculus for financial engineering = an introduction using R / by: (Electronic resources)

R for marketing research and analytics by: (Electronic resources)

Deep learning with R by: (Electronic resources)

R quick syntax reference = a pocket guide to the language, APIs and library / by: (Electronic resources)

Time series analysis and its applications = with R examples / by: (Electronic resources)

R data science quick reference = a pocket guide to APIs, libraries, and packages / by: (Electronic resources)

An introduction to r for spatial analysis and mapping / by: (Language materials, printed)

An introduction to statistical learning = with applications in R / by: (Electronic resources)

Modern optimization with R by: (Electronic resources)

SAS for R users = a book for budding data scientists / by: (Electronic resources)

Introduction to probabilistic and statistical methods with examples in R by: (Electronic resources)

Statistical analysis of network data with R by: (Electronic resources)

R crash course for biologists : = an introduction to R for bioinformatics and biostatistics / by: (Language materials, printed)

Virus host cell genetic material transport = computational ode/pde modeling with R / by: (書目-電子資源)

R 4 quick syntax reference = a pocket guide to the language, API's and library / by: (書目-電子資源)

Beginning data science in R 4 = data analysis, visualization, and modelling for the data scientist / by: (書目-電子資源)

R 4 data science quick reference = a pocket guide to APIs, libraries, and packages / by: (書目-電子資源)

Probability, statistics and simulation = with application programs written in R / by: (書目-電子資源)

A tour of data science = learn R and Python in parallel / by: (書目-電子資源)

Supervised machine learning = optimization framework and applications with SAS and R / by: (書目-電子資源)

Biometry for forestry and environmental data with examples in R by: (書目-電子資源)

Panel data econometrics with R by: (書目-電子資源)

Habitat suitability and distribution models : = with applications in R / by: (書目-語言資料,印刷品)

Hyperparameter tuning for machine and deep learning with R = a practical guide / by: (書目-電子資源)

Uncertainty quantification using R by: (書目-電子資源)

Working with data in public health = a practical pathway with R / by: (書目-電子資源)

Practical business analytics using R and Python = solve business problems using a data-driven approach / by: (書目-電子資源)

Introductory applied statistics = with resampling methods & R / by: (書目-電子資源)

Introduction to stochastic processes using R by: (書目-電子資源)

Probability = with applications and R / by: (書目-電子資源)

Practical time series analysis : = prediction with statistics and machine learning / by: (書目-語言資料,印刷品)

Introduction to probability, statistics & R = foundations for data-based sciences / by: (書目-電子資源)

Uncertainty quantification with R = Bayesian methods / by: (書目-電子資源)

Learning analytics methods and tutorials = a practical guide using R / by: (書目-電子資源)

Handbook of regression modeling in people analytics = with examples in R and Python / by: (書目-電子資源)

Applied regularization methods for the social sciences by: (書目-電子資源)

The R book by: (書目-電子資源)

R programming = statistical data analysis in research / by: (書目-電子資源)

Business analytics with R and Python by: (書目-電子資源)

Humanities data in R = exploring networks, geospatial data, images, and text / by: (書目-電子資源)