R (Computer program language)
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R (Computer program language)
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R (Computer program language)
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Incoming Resources
- Statistical rethinking, a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Statistical analysis of financial data, with examples in R, James E. Gentle
- R quick syntax reference, a pocket guide to the language, APIs and library, Margot Tollefson
- Mathematical statistics with resampling and R, Laura M. Chihara ; Tim C. Hesterberg
- Statistics with R, a beginner's guide, Robert Stinerock
- R in finance and economics, a beginner's guide, Abhay Kumar Singh, David Edmund Allen
- Uncertainty analysis of experimental data with R, Benjamin D. Shaw
- Practical time series analysis, prediction with statistics and machine learning, Aileen Nielsen
- Practical data science cookbook, 89 hands-on recipes to help you complete real-world data science projects in R and Python, Tony Ojeda ... [and others]
- Applied regularization methods for the social sciences, Holmes Finch
- Qualitative comparative analysis (QCA) using R, A Beginner's Guide, Ioana-Elena Oana, Carsten Q. Schneider, Eva Thomann
- Practical Machine Learning in R, Fred Nwanganga, Mike Chapple
- Factor analysis and dimension reduction in R, a social scientist's toolkit, G. David Garson
- Hands-on data analysis in R for finance, Jean-François Collard
- Analyzing linguistic data, a practical introduction to statistics using R, R.H. Baayen
- Machine learning with R, discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R, Brett Lantz
- Spatial econometric methods in agricultural economics using R, Paolo Postiglione, Roberto Benedetti, Federica Piersimoni
- Advanced machine learning with R, tackle data analytics and machine learning challenges and build complex applications with R 3.5, Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
- IFRS 9 and CECL credit risk modelling and validation, a practical guide with examples worked in R and SAS, Tiziano Bellini
- Financial, macro and micro econometrics using R, Hrishikesh D. Vinod, C. R. Rao editors
- Deep learning, from big data to artificial intelligence with R, Stéphane Tufféry
- Time series analysis, with applications in R, Jonathan D. Cryer, Kung-Sik Chan
- Using R for data analysis in social sciences, a research project-oriented approach, Quan Li
- Biostatistics with R, an introductory guide for field biologists, Jan Lepš, Petr Šmilauer
- R companion to elementary applied statistics, Christopher Hay-Jahans
- A survivor's guide to R, an introduction for the uninitiated and the unnerved, Kurt Taylor Gaubatz, Old Dominion University
- Financial analytics with R, building a laptop laboratory for data science, Mark J. Bennett, University of Chicago, Dirk L. Hugen, University of Iowa
- An introduction to the advanced theory of nonparametric econometrics, a replicable approach using R, Jeffrey S. Racine
- Modern applied regressions, Bayesian and frequentist analysis of categorical and limited response variables with R and Stan, Jun Xu
- Applied probabilistic calculus for financial engineering, an introduction using R, Bertram K.C. Chan
- Business statistics with solutions in R, Mustapha Abiodun Akinkunmi
- Integrated population models, theory and ecological applications with R and JAGS, Michael Schaub and Marc Kéry
- R for data science, import, tidy, transform, visualize, and model data, Hadley Wickham and Garrett Grolemund
- A modern approach to regression with R, Simon J. Sheather
- Python for R users, a data science approach, Ajay Ohri
- An introduction to statistical learning, with applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- R graph essentials, use R's powerful graphing capabilities to design and create professional-level graphics, David Alexander Lillis
- Bayesian modeling of spatio-temporal data with R, Sujit Sahu
- Flexible regression and smoothing, using GAMLSS in R, Mikis D. Stasinopoulos [and four others]
- Beginning R 4, from beginner to pro, Matt Wiley, Joshua F. Wiley
- Data analytics for the social sciences, applications in R, G. David Garson
- Data mining algorithms, explained using R, Pawel Cichosz
- Linear models and regression with R, an integrated approach, Debasis Sengupta, Sreenivasa Rao Jammalamadaka
- R cookbook, proven recipes for data analysis, statistics, and graphics, J.D. Long & Paul Teetor
- An introduction to analysis of financial data with R, Ruey S. Tsay
- Data management in R, a guide for social scientists, Martin Elff
- Advanced R, Hadley Wickham
- Statistics using R, an integrative approach, Sharon Lawner Weinberg, Daphna Harel, Sarah Knapp Abramowitz
- Regression for health and social science, applied linear models with R, Daniel Zelterman
- Applied statistics, theory and problem solutions with R, Dieter Rasch, Rob Verdooren, Jürgen Pilz