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R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. Please consult the R project homepage for further information. CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Please use the CRAN mirror nearest to you to minimize network load


References in zbMATH (referenced in 111 articles )

Showing results 1 to 20 of 111.
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  1. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  2. Bernardi, Mauro; Catania, Leopoldo: Comparison of value-at-risk models using the MCS approach (2016)
  3. Biau, Gérard; Fischer, Aurélie; Guedj, Benjamin; Malley, James D.: COBRA: a combined regression strategy (2016)
  4. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: Mlr: machine learning in $\bold R$ (2016)
  5. Blaser, Rico; Fryzlewicz, Piotr: Random rotation ensembles (2016)
  6. Braun, W. John; Murdoch, Duncan J.: A first course in statistical programming with R. (2016)
  7. Chambers, John M.: Extending R (2016)
  8. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)
  9. Dickson, Maria Michela; Tillé, Yves: Ordered spatial sampling by means of the traveling salesman problem (2016)
  10. Friendly, Michael; Meyer, David: Discrete data analysis with R. Visualization and modeling techniques for categorical and count data (2016)
  11. Gerhart, Christoph: A multiple-curve Lévy forward rate model in a two-price economy (2016)
  12. Grillenzoni, Carlo: Design of blurring mean-shift algorithms for data classification (2016)
  13. Köhn, Hans-Friedrich; Chiu, Chia-Yi: A proof of the duality of the DINA model and the DINO model (2016)
  14. Latouche, Pierre; Mattei, Pierre-Alexandre; Bouveyron, Charles; Chiquet, Julien: Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression (2016)
  15. Lin, Jiahe; Basu, Sumanta; Banerjee, Moulinath; Michailidis, George: Penalized maximum likelihood estimation of multi-layered Gaussian graphical models (2016)
  16. Moore, Dirk F.: Applied survival analysis using R (2016)
  17. Morey, Richard D.; Romeijn, Jan-Willem; Rouder, Jeffrey N.: The philosophy of Bayes factors and the quantification of statistical evidence (2016)
  18. Mulder, Joris (ed.); Wagenmakers, Eric-Jan (ed.): Editors’ introduction to the special issue “Bayes factors for testing hypotheses in psychological research: practical relevance and new developments” (2016)
  19. Perthame, Émeline; Friguet, Chloé; Causeur, David: Stability of feature selection in classification issues for high-dimensional correlated data (2016)
  20. Reulen, Holger; Kneib, Thomas: Boosting multi-state models (2016)

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Further publications can be found at: http://journal.r-project.org/