CRAN

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 445 articles , 1 standard article )

Showing results 1 to 20 of 445.
Sorted by year (citations)

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  1. Annarosa Quarello, Olivier Bock, Emilie Lebarbier: A new segmentation method for the homogenisation of GNSS-derived IWV time-series (2020) arXiv
  2. Bajalinov, E.; Duleba, Sz.: Seasonal time series forecasting by the Walsh-transformation based technique (2020)
  3. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  4. Bob Obenchain: Ridge TRACE Diagnostics (2020) arXiv
  5. Bradley C. Saul, Michael G. Hudgens: The Calculus of M-Estimation in R with geex (2020) not zbMATH
  6. Cappozzo, Andrea; Greselin, Francesca; Murphy, Thomas Brendan: A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise (2020)
  7. Chainarong Amornbunchornvej: mFLICA: An R package for Inferring Leadership of Coordination From Time Series (2020) arXiv
  8. Chakraborty, Saptarshi; Paul, Debolina; Das, Swagatam: Hierarchical clustering with optimal transport (2020)
  9. David B. Dahl: Integration of R and Scala Using rscala (2020) not zbMATH
  10. de Castro, Mário; Gómez, Yolanda M.: A Bayesian cure rate model based on the power piecewise exponential distribution (2020)
  11. Ducharme, Gilles R.; Lafaye de Micheaux, Pierre: A goodness-of-fit test for elliptical distributions with diagnostic capabilities (2020)
  12. Fabrizi, Enrico; Salvati, Nicola; Trivisano, Carlo: Robust Bayesian small area estimation based on quantile regression (2020)
  13. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  14. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  15. Gaigall, Daniel: Testing marginal homogeneity of a continuous bivariate distribution with possibly incomplete paired data (2020)
  16. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  17. Grundy, Thomas; Killick, Rebecca; Mihaylov, Gueorgui: High-dimensional changepoint detection via a geometrically inspired mapping (2020)
  18. Haddon, Malcolm: Using R for modelling and quantitative methods in fisheries (2020)
  19. Haider, Humza; Hoehn, Bret; Davis, Sarah; Greiner, Russell: Effective ways to build and evaluate individual survival distributions (2020)
  20. Hao Wang, Diederick Vermetten, Carola Doerr, Thomas Bäck: IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic (2020) arXiv

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