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

Keywords for this software

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References in zbMATH (referenced in 456 articles , 1 standard article )

Showing results 1 to 20 of 456.
Sorted by year (citations)

1 2 3 ... 21 22 23 next

  1. Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
  2. Annarosa Quarello, Olivier Bock, Emilie Lebarbier: A new segmentation method for the homogenisation of GNSS-derived IWV time-series (2020) arXiv
  3. Bajalinov, E.; Duleba, Sz.: Seasonal time series forecasting by the Walsh-transformation based technique (2020)
  4. Bangyao Zhao, Lili Zhao: Data Mining in Large Frequency Tables With Ontology, with an Application to the Vaccine Adverse Event Reporting System (2020) arXiv
  5. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  6. Bob Obenchain: Ridge TRACE Diagnostics (2020) arXiv
  7. Bradley C. Saul, Michael G. Hudgens: The Calculus of M-Estimation in R with geex (2020) not zbMATH
  8. 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)
  9. Chainarong Amornbunchornvej: mFLICA: An R package for Inferring Leadership of Coordination From Time Series (2020) arXiv
  10. Chakraborty, Saptarshi; Paul, Debolina; Das, Swagatam: Hierarchical clustering with optimal transport (2020)
  11. Cheng, Dan; He, Zhibing; Schwartzman, Armin: Multiple testing of local extrema for detection of change points (2020)
  12. David B. Dahl: Integration of R and Scala Using rscala (2020) not zbMATH
  13. de Castro, Mário; Gómez, Yolanda M.: A Bayesian cure rate model based on the power piecewise exponential distribution (2020)
  14. Ducharme, Gilles R.; Lafaye de Micheaux, Pierre: A goodness-of-fit test for elliptical distributions with diagnostic capabilities (2020)
  15. Fabrizi, Enrico; Salvati, Nicola; Trivisano, Carlo: Robust Bayesian small area estimation based on quantile regression (2020)
  16. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  17. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  18. Gaigall, Daniel: Testing marginal homogeneity of a continuous bivariate distribution with possibly incomplete paired data (2020)
  19. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  20. Grundy, Thomas; Killick, Rebecca; Mihaylov, Gueorgui: High-dimensional changepoint detection via a geometrically inspired mapping (2020)

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