R (Computer program language)
概要
| 作品: | 415 作品在 231 項出版品 231 種語言 | |
|---|---|---|
書目資訊
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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Statistical methods for environmental epidemiology with Ra case study in air pollution and health /
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Hands-on intermediate econometrics using R :templates for extending dozens of practical examples /
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Multivariate nonparametric methods with Ran approach based on spatial signs and ranks /
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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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Mastering R for quantitative finance :use R to optimize your trading strategy and build up your own risk management system /
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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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Statistical analysis and data display :an intermediate course with examples in R /
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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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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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Business case analysis with Rsimulation tutorials to support complex business decisions /
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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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Advanced R statistical programming and data modelsanalysis, machine learning, and visualization /
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From experimental network to meta-analysismethods and applications with R for agronomic and environmental sciences /
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Discrete data analysis with R :visualization and modeling techniques for categorical and count data /
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Model-based clustering and classification for data science :with applications in R /
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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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Quantile regression for cross-sectional and time series dataapplications in energy markets using R /
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Applied multiple imputationadvantages, pitfalls, new developments and applications in R /
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Advanced analytics in power BI with R and Pythoningesting, transforming, visualizing /
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Insights from data with R :an introduction for the life and environmental sciences /
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A course on small area estimation and mixed modelsmethods, theory and applications in R /
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Univariate, bivariate, and multivariate statistics using Rquantitative tools for data analysis and data science /
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Pro data visualization using R and Javascriptanalyze and visualize key data on the web /
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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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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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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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Bayesian multilevel models for repeated measures data :a conceptual and practical introduction in R /
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Handbook of graphs and networks in people analytics :with examples in R and python /
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