R (Computer program language)
概要
| 作品: | 252 作品在 168 項出版品 168 種語言 | |
|---|---|---|
書目資訊
Elements of network science = theory, methods and applications in Stata, R and Python /
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R applications in earth sciences = from soil data to climate time series analysis and modeling /
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Combinatorial pattern matching algorithms in computational biology using Perl and R
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Statistical methods for environmental epidemiology with R = a case study in air pollution and health /
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R through Excel = a spreadsheet interface for statistics, data analysis, and graphics /
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Data mining with Rattle and R = the art of excavating data for knowledge discovery /
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Statistical analysis and data display = an intermediate course with examples in R /
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Permutation tests for stochastic ordering and ANOVA = theory and applications with R /
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Introduction to data analysis and graphical presentation in biostatistics with R = statistics in the large /
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Big data analytics with R and Hadoop : = set up an integrated infrastructure of R and Hadoop to turn your data analytics into Big Data analytics /
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Adaptive tests of significance using permutations of residuals with R and SAS DUP_1
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Nonparametric hypothesis testing DUP_1 = rank and permutation methods with applications in R /
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Nonparametric hypothesis testing = rank and permutation methods with applications in R /
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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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Working with the American community survey in R = a guide to using the acs package /
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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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Data science in higher education : = step-by-step machine learning for institutional researchers /
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Metaprogramming in R = advanced statistical programming for data science, analysis and finance /
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Advanced object-oriented programming in R = statistical programming for data science, analysis and finance /
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Applied probabilistic calculus for financial engineering = an introduction using R /
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Data analysis with R : = a comprehensive guide to manipulating, analyzing, and visualizing data in R /
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Corpus linguistics and statistics with R = introduction to quantitative methods in linguistics /
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Computerized adaptive and multistage testing with R = using packages catR and mstR /
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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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Business case analysis with R = simulation tutorials to support complex business decisions /
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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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Pro machine learning algorithms = a hands-on approach to implementing algorithms in Python and R /
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Applied analytics through case studies using SAS and R = implementing predictive models and machine learning techniques /
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Data science and predictive analytics = biomedical and health applications using R /
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Applied probabilistic calculus for financial engineering : = an introduction using R /
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An introduction to the advanced theory of nonparametric econometrics : = a replicable approach using 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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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 questionnaires = a unified approach based on R and Stata /
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Univariate, bivariate, and multivariate statistics using R = quantitative tools for data analysis and data science /
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Retirement income recipes in R : = from ruin probabilities to intelligent drawdowns /
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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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Applied linear regression for business analytics with R = a practical guide to data science with case studies /
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Functional programming in R 4 = advanced 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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Spatio-temporal trend analysis of rainfall using R software and ArcGIS = a case study of an agro-climatic zone-1 of Gujarat, India /
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Data visualization for social and policy research : = a step-by-step approach using R and Python /
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Model-based clustering and classification for data science : = with applications in R /
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Complex-valued econometrics with examples in R = modelling, regression and applications /
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Model-based clustering and classification for data science = with applications in R /
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Handbook of regression modeling in people analytics = with examples in R and Python /
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